Oracle® Data Miner

User's Guide

Release 17.3

E89857-01

September 2017

Oracle Data Miner User's Guide, Release 17.3

E89857-01

Copyright © 2015, 2017, Oracle and/or its affiliates. All rights reserved.

Primary Author: Moitreyee Hazarika

Contributors: Alex Sakharov, Ari Mozes, Charlie Berger , Denny Wong , Dongfang Bai, Eros Espinola, Francisco Noriega, Gloria Castillo, Jim Dadashev, Mark McCracken, Mark Kelly , Marat Spivak, Pedro Reta, Zabdiel Jaramillo Roman

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Contents

List of Figures

List of Tables

Preface

- Audience

- Documentation Accessibility

- Related Documents

- Conventions

New Features and Changes in Oracle Data Miner

- Oracle Data Mining Features

- Association Model Aggregation Metrics

- Enhancements to Algorithm Settings

- Changes to Decision Tree Algorithm Settings

- Changes to Expectation Maximization Algorithm Settings

- Changes to Generalized Linear Models Algorithm Settings

- Changes to k-Means Algorithm Settings

- Changes to Support Vector Machine Algorithm Settings

- Changes to Singular Value Decomposition and Principal Components Analysis Algorithm Settings

- Support for Explicit Semantic Analysis Algorithm

- Explicit Feature Extraction Node

- Feature Compare Node

- Enhancement to Data Mining Model Detail View

- Enhancements to Filter Column Node

- Mining Model Build Alerts

- R Build Model Node

- Support for Partitioned Models

- Oracle Data Miner Features

- Aggregation Node Support for DATE and TIMESTAMP Data Types

- Enhancement to JSON Query Node

- Enhancement to Build Nodes

- Enhancement to Text Settings

- Refresh Input Data Definition

- Support for Additional Data Types

- Support for In-Memory Column

- Support for Workflow Scheduling

- Enhancement to Polling Performance

- Workflow Status Polling Performance Improvement

- Oracle Database Features

1 Oracle Data Miner

- About the Data Mining Process

- Overview of Oracle Data Miner

- Architecture of Oracle Data Miner

- Snippets in Oracle Data Miner

- Using Predictive Analytics Snippets

- About Oracle Data Miner Repository Installation

- Installing the Data Miner Repository Using the GUI

- About Dropping the Oracle Data Miner Repository

- Dropping the Data Miner Repository Using GUI

- About Oracle Data Miner Repository Migration

- Migrating the Oracle Data Miner Repository Using the GUI

- How to Use Oracle Data Miner

- Oracle By Example for Oracle Data Miner 4.1

- Sample Data

- Oracle Data Miner Online Help

- Search the Online Help

- Oracle Data Mining Forum

- Oracle Data Miner Documentation

2 Connections for Data Mining

- About the Data Miner Tab

- Prerequisites for Data Mining

- Viewing the Data Miner Tab

- Creating a Connection

- Creating Connections from the Connections Tab

- Creating Connections from the Data Miner Tab

- Managing a Connection

- Viewing and Editing Connection Properties

- Removing a Connection

- Connecting to a Database

- Provide Password

3 Data Miner Projects

- Creating a Project

- Project Name Restrictions

- Managing a Project

- Deleting a Project

- Expanding a Project

- Project Properties

- Rename Project

- Importing a Workflow

- Import Workflow

4 Workflow

- About Workflows

- Workflow Sequence

- Workflow Terminology

- Workflow Thumbnail

- Components

- Workflow Editor

- Workflow Properties

- Properties

- Working with Workflows

- Creating a Workflow

- Workflow Name Restrictions

- Deploying Workflows

- Deploy Workflows using Data Query Scripts

- Deploy Workflows using Object Generation Scripts

- Running Workflow Scripts

- Deleting a Workflow

- Loading a Workflow

- Managing a Workflow

- Exporting a Workflow Using the GUI

- Import Requirements of a Workflow

- Data Table Names

- Workflow Compatibility

- Building and Modifying Workflows

- Missing Tables or Views

- Managing Workflows using Workflow Controls

- Managing Workflows and Nodes in the Properties Pane

- Performing Tasks from Workflow Context Menu

- Oracle Enterprise Manager Jobs

- Renaming a Workflow

- Runtime Requirements

- Running a Workflow

- Network Connection Interruption

- Locking and Unlocking Workflows

- Scheduling a Workflow

- Create Schedule

- Repeat

- Repeat Hourly

- Repeat Daily

- Repeat Weekly

- Repeat Monthly

- Repeat Yearly

- Schedule

- Save a Schedule

- Advanced Settings

- Workflow Prerequisites

- Workflow Script Requirements

- Script File Character Set Requirements

- Script Variable Definitions

- Scripts Generated

- Running Scripts using SQL*Plus or SQL Worksheet

- About Nodes

- Node Name and Node Comments

- Node Types

- Node States

- Working with Nodes

- Add Nodes or Create Nodes

- Copy Nodes

- Edit Nodes

- Editing Nodes through Edit Dialog Box

- Editing Nodes through Properties

- Link Nodes

- Linking Nodes in Components Pane

- Node Connection Dialog Box

- Change Node Position

- Connect Option in Diagram Menu

- Deleting a Link

- Cancelling a Link

- Refresh Nodes

- Run Nodes

- Performing Tasks from the Node Context Menu

- Connect

- Run

- Force Run

- Create Schedule

- Edit

- View Data

- View Models

- Generate Apply Chain

- Refresh Input Data Definition

- Show Event Log

- Deploy

- Show Graph

- Cut

- Copy

- Paste

- Extended Paste

- Select All

- Performance Settings

- Toolbar Actions

- Show Runtime Errors

- Show Validation Errors

- Save SQL

- Validate Parents

- Compare Test Results

- View Test Results

- Go to Properties

- Navigate

- About Parallel Processing

- Parallel Processing Use Cases

- Premise of the Parallel Processing Use Case

- Making Transformation Run Faster, using Parallel Processing and Other Methods

- Running Graph Node in Parallel

- Running a Node in Parallel to Test Performance

- Oracle Data Mining Support for Parallel Processing

- Setting Parallel Processing for a Node or Workflow

- Performance Settings

- Edit Node Performance Settings

- Edit Node Parallel Settings

- About Oracle Database In-Memory

- Benefits of Oracle Database In-Memory Column Store

- Use Cases of Oracle Database In-Memory

5 Data Nodes

- Create Table or View Node

- Working with the Create Table or View Node

- Creating a Create Table or View Node

- Create Table Node and Compression

- Edit Create Table or View Node

- Edit Storage Settings

- Select Columns

- View Create Table or View Data

- Create Table or View Context Menu

- Connect

- Run

- Force Run

- Create Schedule

- Edit

- View Data

- View Models

- Generate Apply Chain

- Refresh Input Data Definition

- Show Event Log

- Deploy

- Show Graph

- Cut

- Copy

- Paste

- Extended Paste

- Select All

- Performance Settings

- Toolbar Actions

- Show Runtime Errors

- Show Validation Errors

- Save SQL

- Validate Parents

- Compare Test Results

- View Test Results

- Go to Properties

- Navigate

- Create the Table or View Node Properties

- Table

- Columns

- Automatic Behavior

- Data Source Node

- Supported Data Types for Data Source Nodes

- Support for Date and Time Data

- Working with the Data Source Node

- Create a Data Source Node

- Edit Schema List

- Define a Data Source Node

- Edit Data Guide

- Select Data Guide

- JSON Settings

- Edit a Data Source Node

- Running a Data Source Node

- Data Source Node Context Menu

- Select Attributes

- Data Source Node Viewer

- Data

- Select Column to Sort By

- Graph

- Columns

- SQL

- Data Source Node Properties

- Data

- Cache

- Details

- Explore Data Node

- Create an Explore Data Node

- Edit the Explore Data Node

- Input (Explore)

- Select Attributes

- Statistics (Explore)

- Mode (Sampled)

- Explore Data Node Viewer

- Statistics

- Columns

- Data

- SQL

- Export Node Calculations

- Perform Tasks from the Explore Data Node Context Menu

- Explore Data Node Properties

- Input (Properties)

- Statistics

- Output

- Histogram

- Sample

- Graph Node

- Types of Graphs

- Box Plot or Box Graph

- Supported Data Types for Graph Nodes

- Graph Node Context Menu

- Running Graph Node

- Show Graph

- Create a Graph Node

- New Graph

- Line or Scatter

- Bar

- Histogram

- Box

- Settings (Graph Node)

- Select Values to Display

- Axis Treatment

- Graph Node Editor

- Zoom Graph

- Viewing Data used to Create Graph

- Edit Graph

- Bar

- Box

- Histogram

- Line or Scatter

- Graph Node Properties

- Cache (Graph Node)

- Details

- Data

- SQL Query Node

- Input for SQL Query Node

- SQL Query Restriction

- Create a SQL Query Node

- SQL Query Node Editor

- Oracle R Enterprise Script Support

- Oracle R Enterprise Database Roles

- SQL Query Node Context Menu

- SQL Query Node Properties

- Update Table Node

- Input and Output for Update Table Node

- Data Types for Update Table Node

- Create Update Table Node

- Update Table Node Automatic Behavior

- Edit Update Table Node

- Create Table (Update Table)

- Select Attributes (Update Table)

- Edit Columns (Update Table)

- Update Table Node Data Viewer

- Update Table Node Context Menu

- Update Table Node Properties

- Table (Update Table)

- Columns (Update Table)

- Cache

- Details

- Target Values Selection

6 Using the Oracle Data Miner GUI

- Graphical User Interface Overview

- Oracle Data Miner Functionality in the Menu Bar

- View Menu

- Data Miner

- Workflow Jobs

- Scheduled Jobs

- Structure Window

- Structure Window and Workflow

- Structure Window and Model Viewers

- Structure Window Controls

- Tools Menu

- Data Miner

- Data Miner Preferences

- Node Settings

- Viewers

- Workflow Editor

- Workflow Import/Export

- Workflow Jobs

- Workflow Scheduler

- Diagram Menu

- Connect

- Align

- Distribute

- Bring to Front

- Send to Back

- Zoom

- Publish Diagram

- Oracle Data Miner Online Help

- Workflow Jobs

- Viewing Workflow Jobs

- Working with Workflow Jobs

- Workflow Jobs Grid

- View an Event Log

- Informational Messages

- Workflow Jobs Context Menu

- Projects

- Miscellaneous

- Filter

- Import Data (Oracle Data Miner)

- Filter Out Objects Associated with Oracle Data Mining

- Copy Charts, Graphs, Grids, and Rules

7 Transforms Nodes

- Aggregation

- Creating Aggregate Nodes

- Editing Aggregate Nodes

- Edit Group By

- Define Aggregation

- Edit Aggregate Element

- Add Column Aggregation

- Add Custom Aggregation

- Aggregate Node Properties

- Cache

- Details

- Aggregate Node Context Menu

- Data Viewer

- Data

- Select Column to Sort By

- Graph

- Columns

- SQL

- Expression Builder

- Functions

- Filter Columns Node

- Creating Filter Columns Node

- Editing Filter Columns Node

- Exclude Columns

- Define Filter Columns Settings

- Explore Dependencies

- Predictor Dependencies

- Performing Tasks After Running Filter Columns Node

- Columns Filter Details Report

- Attribute Importance

- Attribute Importance Viewer

- Filter Columns Node Properties

- Filter Columns Node Context Menu

- Filter Columns Details

- Creating the Filter Columns Details Node

- Editing the Filter Columns Details Node

- Filter Columns Details Node Properties

- Filter Columns Details Node Context Menu

- Filter Rows

- Creating a Filter Rows Node

- Edit Filter Rows

- Filter

- Columns

- Filter Rows Node Properties

- Filter Rows Node Context Menu

- Join

- Create a Join Node

- Edit a Join Node

- Edit Join Node

- Edit Columns

- Edit Output Data Column

- Resolve

- Join Node Properties

- Join Node Context Menu

- JSON Query

- Create JSON Query Node

- JSON Query Node Editor

- JSON

- Structure

- Data

- Filter Settings

- Additional Output

- Edit Output Data Column Dialog

- Aggregate

- Add Aggregations

- Edit Sub Group By

- Edit Group By

- Preview

- Output Columns

- Output Data

- JSON Query Node Properties

- Output

- Cache

- Details

- JSON Query Node Context Menu

- Data Types and their Supported Operators

- Sample

- Sample Nested Data

- Creating a Sample Node

- Edit Sample Node

- Random

- Top N

- Stratified

- Custom Balance

- Sample Node Properties

- Cache

- Details

- Sample Node Context Menu

- Transform

- Supported Transformations

- Binning

- Recode

- Custom

- Missing Values

- Normalization

- Outlier

- Support for Date and Time Data Types

- Creating Transform Node

- Edit Transform Node

- Add Transform

- Binning

- Bin Equal Width (Number)

- Bin Quantile

- Bin Top N

- Custom

- Missing Values

- Normalization

- Outlier

- Use Existing Column

- Add or Edit Multiple Transforms

- Add Custom Transform

- Apply Transform Wizard

- Define Columns

- Edit Transform

- Edit Custom Transform

- Transform Node Properties

- Transform Node Context Menu

8 Model Nodes

- Types of Models

- Automatic Data Preparation (ADP)

- Numerical Data Preparation

- Manual Data Preparation

- Data Used for Model Building

- Viewing and Changing Data Usage

- Input Tab of Build Editor

- Automatic Input

- Manual Input

- Advanced Settings

- Text

- Model Nodes Properties

- Models

- Output Column

- Add Model

- Build

- Test

- Details

- Anomaly Detection Node

- Create Anomaly Detection Node

- Edit Anomaly Detection Node

- Build (AD)

- Partition

- Input

- Sampling

- Text

- Data for Model Build

- Advanced Model Settings

- Add Model (AD)

- Data Usage

- Anomaly Detection Node Properties

- Models (AD)

- Output Column (AD)

- Add Model (AD)

- Build (AD)

- Partition

- Details

- Anomaly Detection Node Context Menu

- Association Node

- Behavior of the Association Node

- Create Association Node

- Edit Association Build Node

- Build

- Select Columns (AR)

- Partition

- Advanced Settings

- Filter

- Find Items

- Aggregates

- Sampling

- Advanced Settings for Association Node

- Association Node Context Menu

- Association Build Properties

- Models (AR)

- Add Model (AR)

- Output Column (AR)

- Build (AR)

- Partition

- Filter

- Aggregates

- Sampling

- Details

- Classification Node

- Default Behavior for Classification Node

- Create a Classification Node

- Data for Model Build

- Edit Classification Build Node

- Build (Classification)

- No Case ID

- Partition

- Add Partition Column

- Sampling

- Input

- Text

- Advanced Settings for Classification Models

- Add Models

- Add Model (Classification)

- Classification Node Properties

- Classification Node Models

- Classification Node Output Column

- Classification Node Build

- Classification Node Test

- Target Values Selection

- Partition

- Details

- Classification Build Node Context Menu

- View Test Results

- Compare Test Results

- Clustering Node

- Default Behavior for Clustering Node

- Create Clustering Build Node

- Data for Model Build

- Edit Clustering Build Node

- Build (Clustering)

- Add Model (Clustering)

- Partition

- Sampling

- Input

- Text

- Advanced Settings for Clustering Models

- Clustering Build Node Properties

- Models

- Add Model (Clustering)

- View Models

- Clustering Node Output Column

- Build

- Partition

- Sampling

- Details

- Clustering Build Node Context Menu

- Explicit Feature Extraction Node

- Create Explicit Feature Extraction Node

- Edit Explicit Feature Extraction Node

- Build

- Add Model

- Partition

- Add Partitioning Columns

- Sampling

- Input

- Text

- Advanced Model Settings

- Explicit Feature Extraction Build Properties

- Models

- Build

- Partition

- Details

- Explicit Feature Extraction Context Menu

- Feature Extraction Node

- Default Behavior of Feature Extraction Node

- Create Feature Extraction Node

- Data for Model Build

- Edit Feature Extraction Build Node

- Build (Feature Extraction)

- Add Model (Feature Extraction)

- Partition

- Sampling

- Input

- Text

- Stoplist Details

- Add Stopwords Stopthemes from Features

- Advanced Settings for Feature Extraction

- Feature Extraction Node Properties

- Feature Extraction Node Context Menu

- Model Node

- Create a Model Node

- Edit Model Selection

- Model Constraints

- Model Node Properties

- Models (Model Node)

- Details

- Model Node Context Menu

- Model Details Node

- Model Details Node Input and Output

- Create Model Details Node

- Edit Model Details Node

- Edit Model Selection Details

- Model Details Automatic Specification

- Default Model and Output Type Selection

- Model Details Node Properties

- Models (Model Details)

- Output (Model Details)

- Cache (Model Details)

- Details

- Model Details Node Context Menu

- View Data (Model Details)

- Model Details Per Model

- R Build Node

- Create R Build Node

- Edit R Build Node

- Build

- Add Model (R Build Node)

- Partition

- Add Partitioning Columns

- Input

- Sampling

- Text

- Advanced Settings (R Build Node)

- R Build Node Properties

- R Build Node Context Menu

- Regression Node

- Default Behavior for Regression Node

- Create a Regression Node

- Data for Model Build

- Edit Regression Build Node

- Build

- Add Model (Regression)

- Partition

- Sampling

- Input

- Text

- Advanced Settings for Regression Models

- Regression Node Properties

- Models (Regression)

- Output Column

- Build (Regression)

- Partition

- Details

- Test (Regression)

- Regression Node Context Menu

- Advanced Settings Overview

- Upper Pane of Advanced Settings

- Lower Pane of Advanced Settings

- Data Usage

- Algorithm Settings

- Performance Settings

- Mining Functions

- Classification

- Building Classification Models

- Comparing Classification Models

- Applying Classification Models

- Classification Algorithms

- Regression

- Building Regression Models

- Applying Regression Models

- Regression Algorithms

- Anomaly Detection

- Building Anomaly Detection Models

- Applying Anomaly Detection Models

- Clustering

- Using Clusters

- Calculating Clusters

- Algorithms for Clustering

- Association

- Transactions

- Feature Extraction and Selection

- Feature Selection

- Feature Extraction

9 Model Operations

- Apply Node

- Apply Preferences

- Apply Node Input

- Apply Node Output

- Creating an Apply Node

- Apply and Output Specifications

- Edit Apply Node

- Predictions

- Add or Edit Apply Output Column

- Add Output Apply Column Dialog

- Define Apply Columns Wizard

- Models

- Output Specifications

- Define Top N

- Additional Output

- Evaluate and Apply Data

- Edit Apply Node

- Predictions

- Additional Output

- Apply Columns

- Apply Node Properties

- Apply Node Context Menu

- Connect

- Run

- Force Run

- Create Schedule

- Edit

- View Data

- Generate Apply Chain

- Show Event Log

- Show Validation Errors

- Validate Parents

- Deploy

- Save SQL

- Cut

- Copy

- Paste

- Select All

- Performance Settings

- Go to Properties

- Navigate

- Apply Data Viewer

- Feature Compare Node

- Create Feature Compare Node

- Feature Compare

- Feature Compare Node Context Menu

- Connect

- Run

- Force Run

- Create Schedule

- Edit

- View Data

- Generate Apply Chain

- Show Event Log

- Show Validation Errors

- Validate Parents

- Deploy

- Save SQL

- Cut

- Copy

- Paste

- Select All

- Performance Settings

- Go to Properties

- Navigate

- Test Node

- Support for Testing Classification and Regression Models

- Test Node Input

- Automatic Settings

- Creating a Test Node

- Edit Test Node

- Select Model

- Compare Test Results Viewer

- Test Node Properties

- Models

- Selected Models

- Test

- Details

- Test Node Context Menu

10 Predictive Query Nodes

- Anomaly Detection Query

- Create an Anomaly Detection Query Node

- Edit an Anomaly Detection Query

- Edit Anomaly Prediction Output

- Add Anomaly Function

- Edit Anomaly Function Dialog

- Anomaly Detection Query Properties

- Anomaly Detection Query Context Menu

- Connect

- Run

- Force Run

- Create Schedule

- Edit

- View Data

- Generate Apply Chain

- Show Event Log

- Deploy

- Cut

- Copy

- Paste

- Extended Paste

- Select All

- Performance Settings

- Toolbar Actions

- Show Runtime Errors

- Show Validation Errors

- Save SQL

- Validate Parents

- Go to Properties

- Clustering Query

- Create a Clustering Query

- Edit a Clustering Query

- Edit Cluster Prediction Outputs

- Add Cluster Function

- Edit Cluster Function

- Clustering Query Properties

- Clustering Query Context Menu

- Connect

- Run

- Force Run

- Create Schedule

- Edit

- View Data

- Generate Apply Chain

- Show Event Log

- Deploy

- Cut

- Copy

- Paste

- Extended Paste

- Select All

- Performance Settings

- Toolbar Actions

- Show Runtime Errors

- Show Validation Errors

- Save SQL

- Validate Parents

- Go to Properties

- Feature Extraction Query

- Create a Feature Extraction Query

- Edit Feature Extraction Query

- Edit Feature Prediction Outputs

- Add Feature Function

- Edit Feature Function

- Feature Extraction Query Properties

- Feature Extraction Query Context Menu

- Connect

- Run

- Force Run

- Create Schedule

- Edit

- View Data

- Generate Apply Chain

- Show Event Log

- Deploy

- Cut

- Copy

- Paste

- Extended Paste

- Select All

- Performance Settings

- Toolbar Actions

- Show Runtime Errors

- Show Validation Errors

- Save SQL

- Validate Parents

- Go to Properties

- Prediction Query

- Create a Prediction Query

- Edit a Prediction Query

- Add Target

- Add Partitioning Columns

- Add Partitioning Expressions

- Edit Prediction Output

- Add Prediction Output Function

- Edit Prediction Function Dialog

- Modify Input

- View Heuristic Results Details

- Add Additional Output

- Add Supplemental Dialog

- Run Predictive Query Node

- View Data for a Predictive Query

- View Prediction Details

- Prediction Query Properties

- Prediction Query Node Context Menu

- Connect

- Run

- Force Run

- Create Schedule

- Edit

- View Data

- Generate Apply Chain

- Show Event Log

- Deploy

- Cut

- Copy

- Paste

- Extended Paste

- Select All

- Performance Settings

- Toolbar Actions

- Show Runtime Errors

- Show Validation Errors

- Save SQL

- Validate Parents

- Go to Properties

11 Text Nodes

- Oracle Text Concepts

- Text Mining in Oracle Data Mining

- Data Preparation for Text

- Text Processing in Oracle Data Mining 12c Release 1 (12.1) and Later

- Text Processing in Oracle Data Mining 11g Release 2 (11.2) and Earlier

- Apply Text Node

- Default Behavior for the Apply Text Node

- Create an Apply Text Node

- Edit Apply Text Node

- View the Text Transform (Apply)

- Apply Text Node Properties

- Transforms

- Cache

- Sample

- Details

- Apply Text Node Context Menu

- Build Text

- Default Behavior of the Build Text Node

- Create Build Text Node

- Edit Build Text Node

- View the Text Transform

- Add/Edit Text Transform

- Stoplist Editor

- New Stoplist Editor

- Add Stopwords/Stopthemes

- Build Text Node Properties

- Transforms

- Sample

- Cache

- Details

- Build Text Node Context Menu

- Text Reference

- Create a Text Reference Node

- Edit Text Reference Node

- Select Build Text Node

- Text Reference Node Properties

- Transforms

- Details

- Text Reference Node Context Menu

12 Testing and Tuning Models

- Testing Classification Models

- Test Metrics for Classification Models

- Performance

- Predictive Confidence

- Average Accuracy

- Overall Accuracy

- Cost

- Performance Matrix

- Receiver Operating Characteristics (ROC)

- How to Use ROC

- Lift

- Profit and ROI

- Profit and ROI Example

- Profit and ROI Use Case

- Compare Classification Test Results

- Compare Test Results

- Edit Test Selection (Classification and Regression)

- Classification Model Test Viewer

- Performance

- Performance Matrix

- Show Detail

- Compare Models

- ROC

- Edit Test Result Selection

- ROC Detail Dialog

- Lift

- Lift Detail

- Profit

- Profit Detail Dialog

- Profit Setting Dialog

- Model Partitions

- Select Partition

- Viewing Test Results

- Tuning Classification Models

- Remove Tuning

- Cost

- Costs and Benefits

- Costs

- Benefits

- Benefit

- ROC

- ROC Tuning Steps

- Select Custom Operating Point

- Receiver Operating Characteristics

- Lift

- About Lift

- Profit

- Profit Setting

- Profit

- Testing Regression Models

- Residual Plot

- Regression Statistics

- Compare Regression Test Results

- Compare Test Results

- Regression Model Test Viewer

- Performance (Regression)

- Residual

13 Data Mining Algorithms

- Anomaly Detection

- Applying Anomaly Detection Models

- Algorithm Settings for AD

- Anomaly Detection Algorithm Settings for Linear or System Determined Kernel

- Active Learning

- Complexity Factor

- Rate of Outliers

- Tolerance Value

- Anomaly Detection Algorithm Settings for Gaussian Kernel

- Active Learning

- Cache Size (Gaussian Kernel)

- Complexity Factor

- Rate of Outliers

- Standard Deviation (Gaussian Kernel)

- Tolerance Value

- Anomaly Detection Model Viewer

- AD Model Viewer for Gaussian Kernel

- Settings (AD)

- Input (AD)

- Anomaly Detection Algorithm Settings

- AD Model Viewer for Linear Kernel

- Coefficients (SVMC Linear)

- Settings (AD)

- Compare (SVMC Linear)

- Viewing Models in Model Viewer

- Association

- Calculating Associations

- Itemsets

- Association Rules

- Data for AR Models

- Support for Text (AR)

- Troubleshooting AR Models

- Algorithm Settings for Association Rules

- AR Model Viewer

- AR Rules

- AR Rules Grid

- AR Rules Display

- Rule Details

- Sorting

- Filtering

- Item Filters

- Add Item Filter

- Itemsets

- Itemsets Display

- Itemset Details

- Settings (AR)

- Summary

- Viewing Models in Model Viewer

- Decision Tree

- Decision Tree Algorithm

- Decision Tree Rules

- Build, Test, and Apply Decision Tree Models

- Decision Tree Algorithm Settings

- Decision Tree Model Viewer

- Save Rules

- Settings (DT)

- DT Summary

- DT Inputs

- Partition Keys

- DT Target Values

- Expectation Maximization

- Build and Apply an EM Model

- EM Algorithm Settings

- EM Data Preparation and Analysis Settings

- EM Model Viewer

- EM, KM, and OC Tree Viewer

- Cluster (Viewer)

- Cluster Model Settings (Viewer)

- EM, KM, and OC Compare

- EM Component

- EM Details

- Explicit Semantic Analysis

- Uses of Algorithm

- Supported Mining Models

- ESA Algorithm Settings

- ESA Model Viewer

- Features

- Settings (ESA)

- Coefficients ESA

- Generalized Linear Models

- Generalized Linear Models Overview

- Linear Regression

- Logistic Regression

- Data Preparation for GLM

- GLM Classification Models

- GLM Classification Algorithm Settings

- Feature Selection Option Dialog

- Choose Reference Value (GLMC)

- Ridge Regression Option Dialog (GLMC)

- GLM Classification Model Viewer

- GLMC Details

- GLMC Coefficients

- Sort and Search GLMC Coefficients

- GLMC Compare

- GLMC Diagnostics

- GLMC Settings

- Summary

- Inputs

- Partition Keys

- Weights

- GLMC Target Values

- GLM Regression Models

- GLM Regression Algorithm Settings

- Ridge Regression Option Dialog (GLMR)

- Choose Reference Value (GLMR)

- GLM Regression Model Viewer

- GLMR Coefficients

- GLMR Details

- GLMR Diagnostics

- GLMR Settings

- GLMR Summary

- GLMR Inputs

- GLMR Coefficients

- GLMR Details

- GLMR Diagnostics

- k-Means

- k-Means Algorithm

- KM Algorithm Settings

- KM Model Viewer

- EM, KM, and OC Tree Viewer

- Cluster (Viewer)

- EM, KM, and OC Compare

- Compare Cluster with Population

- Missing Histograms in a Cluster

- Rename Cluster

- KM Settings

- Cluster Model Settings (Viewer)

- Naive Bayes

- Naive Bayes Algorithm

- Advantages of Naive Bayes

- Naive Bayes Test Viewer

- Naive Bayes Model Viewer

- Probabilities (NB)

- Grid

- Fetch Size

- Grid Filter

- Compare (NB)

- Settings (NB)

- Settings (NB)

- Summary (NB)

- Input (NB)

- Partition Keys

- Weights

- Target Values

- Nonnegative Matrix Factorization

- Using Nonnegative Matrix Factorization

- How Does Nonnegative Matrix Factorization Work

- NMF Algorithm Settings

- NMF Model Viewer

- Coefficients (NMF)

- Rename (NMF)

- Filter (NMF)

- Features

- Settings (NMF)

- Summary (NMF)

- Inputs (NMF)

- Orthogonal Partitioning Clustering

- O-Cluster Algorithm

- OC Algorithm Settings

- OC Model Viewer

- EM, KM, and OC Tree Viewer

- Cluster (Viewer)

- EM, KM, and OC Compare

- Detail (OC)

- Settings (OC)

- Summary (OC)

- Inputs (OC)

- Interpreting Cluster Rules

- Singular Value Decomposition and Principal Components Analysis

- Build and Apply SVD and PCA Models

- PCA Algorithm Settings

- Solver (Stochastic QR Computation)

- PCA Model Viewer

- Coefficients (PCA)

- Rename (PCA)

- Filter (PCA)

- PCA Scree Plot

- Features

- PCA Details

- Settings (PCA)

- Summary (PCA)

- Inputs (PCA)

- SVD Algorithm Settings

- SVD Model Viewer

- Coefficients (SVD)

- Rename (SVD)

- Filter (SVD)

- Features

- SVD Singular Values

- SVD Details

- Settings (SVD)

- Summary (SVD)

- Inputs (SVD)

- Support Vector Machine

- Support Vector Machine Algorithms

- How Support Vector Machines Work

- SVM Kernel Functions

- Building and Testing SVM Models

- SVM Classification Models

- SVM Weights

- SVM Regression Models

- SVM Anomaly Detection Models

- Applying SVM Models

- Applying One-Class SVM Models

- SVM Classification Algorithm Settings

- Algorithm Settings for Linear or System Determined Kernel (SVMC)

- Solver (Sub-Gradient Descend)

- Algorithm Settings for Gaussian Kernel (SVMC)

- Active Learning

- Complexity Factor

- Tolerance Value

- Cache Size (Gaussian Kernel)

- Standard Deviation (Gaussian Kernel)

- SVM Classification Test Viewer

- SVM Classification Model Viewer

- SVMC Model Viewer for Models with Linear Kernel

- Coefficients (SVMC Linear)

- Compare (SVMC Linear)

- Settings (SVMC)

- SVMC Model Viewer for Models with Gaussian Kernel

- Summary (SVMC)

- Inputs (SVMC)

- Weights (SVMC)

- Target Values (SVMC)

- Coefficients (SVMC Linear)

- Coefficients Grid (SVMC)

- Compare (SVMC Linear)

- Search

- Propensity

- Settings (SVMC)

- Summary (SVMC)

- Inputs (SVMC)

- Partition Keys

- Weights (SVMC)

- Algorithm Settings for SVMC

- SVM Regression Algorithm Settings

- Algorithm Settings for Linear or System Determined Kernel (SVMR)

- Tolerance Value

- Active Learning

- Complexity Factor

- Algorithm Settings for Gaussian Kernel (SVMR)

- Tolerance Value

- Complexity Factor

- Active Learning

- Standard Deviation (Gaussian Kernel)

- Cache Size (Gaussian Kernel)

- Automatic Data Preparation

- SVM Regression Test Viewer

- SVM Regression Model Viewer

- SVMR Model Viewer for Models with Linear Kernel

- Coefficients (SVMR)

- Settings (SVMR)

- SVMR Model Viewer for Models with Gaussian Kernel

- Summary (SVMR)

- Inputs (SVMR)

- Settings Information

- General Settings

- Automatic Data Preparation

- Other Settings

- Epsilon Value

Index

List of Figures

- 1-1 Sample Data Miner Workflow

- 1-2 Oracle Data Miner Components

List of Tables

- 4-1 Nodes and Script Functionality

- 4-2 Types of Nodes

- 4-3 Node States

- 7-1 Commonly Used Operators

- 7-2 Data Types and their Supported Operators

Preface

This document contains an overview of Oracle Data Miner. Oracle Data Miner 17.3 is packaged with Oracle SQL Developer 17.3 and later.

- Audience

- Documentation Accessibility

- Related Documents

- Conventions

Audience

This document is intended for data analysts whose primary goal is to perform traditional data mining (build, test, and apply models) using data that resides in an Oracle Database. They are not programmers or database administrators.

Documentation Accessibility

For information about Oracle's commitment to accessibility, visit the Oracle Accessibility Program website at http://www.oracle.com/pls/topic/lookup?ctx=acc&id=docacc.

Access to Oracle Support

Oracle customers that have purchased support have access to electronic support through My Oracle Support. For information, visit http://www.oracle.com/pls/topic/lookup?ctx=acc&id=info or visit http://www.oracle.com/pls/topic/lookup?ctx=acc&id=trs if you are hearing impaired.

Related Documents

For more information, see the following documents in the Documentation Library.

For Oracle Database 12c Release 2 (12.2):

- Oracle Data Mining Concepts

- Oracle Data Mining User’s Guide

For Oracle Database 12c Release 2 (12.1):

- Oracle Data Mining Concepts

- Oracle Data Mining User’s Guide

Conventions

The following text conventions are used in this document:

Convention

Meaning

boldface

Boldface type indicates graphical user interface elements associated with an action, or terms defined in text or the glossary.

italic

Italic type indicates book titles, emphasis, or placeholder variables for which you supply particular values.

monospace

Monospace type indicates commands within a paragraph, URLs, code in examples, text that appears on the screen, or text that you enter.

New Features and Changes in Oracle Data Miner

Oracle Data Miner 17.3 comprises these new features, and general enhancements.

New features include:

- Oracle Data Mining Features

- Oracle Data Miner Features

- Oracle Database Features

Oracle Data Mining Features

The new Oracle Data Mining features include:

- Association Model Aggregation Metrics
Oracle Data Miner supports the enhanced Association Rules algorithm and allows the user to filter items before building the Association model.

- Enhancements to Algorithm Settings
Oracle Data Miner has been enhanced to support enhancements in Oracle Data Mining that includes build settings for building partition models, sampling of training data, numeric data preparation that includes shift and scale transformations, and so on.

- Support for Explicit Semantic Analysis Algorithm
Oracle Data Miner 17.3 supports a new feature extraction algorithm called Explicit Semantic Analysis algorithm.

- Enhancement to Data Mining Model Detail View
The model viewers in Oracle Data Miner have been enhanced to reflect the changes in Oracle Data Mining.

- Enhancements to Filter Column Node
Oracle Data mining supports unsupervised Attribute Importance ranking. The Attribute Importance ranking of a column is generated without the need for selecting a target column. The Filter Column node has been enhanced to support unsupervised Attribute Importance ranking.

- Mining Model Build Alerts
Oracle Data Miner logs alerts related to model builds in the model viewers and event logs.

- R Build Model Node
Oracle Data Mining provides the feature to add R model implementations within the Oracle Data Mining framework. To support R model integration, Oracle Data Miner has been enhanced with a new R Build node with mining functions such as Classification, Regression, Clustering, and Feature Extraction.

- Support for Partitioned Models
Oracle Data Miner supports the building and testing of partitioned models.

Association Model Aggregation Metrics

Oracle Data Miner supports the enhanced Association Rules algorithm and allows the user to filter items before building the Association model.

The user can set the filters in the Association Build node editor, Association model viewer, and Model Details node editor.

Related Topics

- Filter

Enhancements to Algorithm Settings

Oracle Data Miner has been enhanced to support enhancements in Oracle Data Mining that includes build settings for building partition models, sampling of training data, numeric data preparation that includes shift and scale transformations, and so on.

Note:
These settings are available if Oracle Data Miner 17.3 is connected to Oracle Database 12.2.

Changes to the algorithms include:

- Changes to Decision Tree Algorithm Settings
The setting Maximum Supervised Bins CLAS_MAX_SUP_BINS is added in the Decision Tree algorithm.

- Changes to Expectation Maximization Algorithm Settings
The setting Level of Detailsreplaces the current setting Gather Cluster Statistics.

- Changes to Generalized Linear Models Algorithm Settings
The following changes are included in the Generalized Linear Model algorithm settings. The changes apply to both Classification models and Regression models.

- Changes to k-Means Algorithm Settings
The following changes are incorporated to the k-Means algorithm settings.

- Changes to Support Vector Machine Algorithm Settings
The following changes are included in the Support Vector Machine algorithm settings. The changes are applicable to both Linear and Gaussian kernel functions.

- Changes to Singular Value Decomposition and Principal Components Analysis Algorithm Settings
The following changes are included in the Singular Value Decomposition and Principal Components Analysis algorithm.

Changes to Decision Tree Algorithm Settings

The setting Maximum Supervised Bins CLAS_MAX_SUP_BINS is added in the Decision Tree algorithm.

Changes to Expectation Maximization Algorithm Settings

The setting Level of Detailsreplaces the current setting Gather Cluster Statistics.

The underlying algorithm setting used is EMCS_CLUSTER_STATISTICS where All=ENABLE, and Hierarchy=DISABLE. Some additional settings are added and some settings are deprecated.

Settings Added:

- Random Seed

- Model Search

- Remove Small Components

Settings Deprecated:

Approximate Computation ODMS_APPROXIMATE_COMPUTATION

Changes to Generalized Linear Models Algorithm Settings

The following changes are included in the Generalized Linear Model algorithm settings. The changes apply to both Classification models and Regression models.

Settings Added:

- Convergence Tolerance GLMS_CONV_TOLERANCE

- Number of Iterations GLMS_NUM_ITERATIONS

- Batch Rows GLMS_BATCH_ROWS

- Solver GLMS_SOLVER

- Sparse Solver GLMS_SPARSE_SOLVER

Settings Deprecated:

- Approximate Computation ODMS_APPROXIMATE_COMPUTATION

- Categorical Predictor Treatment GLMS_SELECT_BLOCK

- Sampling for Feature Identification GLMS_FTR_IDENTIFICATION

- Feature Acceptance GLMS_FTR_ACCEPTANCE

Changes to k-Means Algorithm Settings

The following changes are incorporated to the k-Means algorithm settings.

Settings Added:

- Levels of Details KMNS_DETAILS

- Random Seeds KMNS_RANDOM_SEEDS

Settings Deprecated:

- Growth Factor

Changes to Support Vector Machine Algorithm Settings

The following changes are included in the Support Vector Machine algorithm settings. The changes are applicable to both Linear and Gaussian kernel functions.

Settings Added:

- Solver SVMS_SOLVER

- Number of Iterations SVMS_NUM_ITERATIONS

- Regularizer SVMS_REGULARIZER

- Batch RowsSVMS_BATCH_ROWS

- Number of Pivots SVMS_NUM_PIVOTS

Note:
Applies to Gaussian kernel function only.

Settings Deprecated:

- Active Learning

- Cache Size SVMS_KERNEL_CACHE_SIZE

Note:
Applies to Gaussian kernel function only.

Changes to Singular Value Decomposition and Principal Components Analysis Algorithm Settings

The following changes are included in the Singular Value Decomposition and Principal Components Analysis algorithm.

Settings Added:

- Solver SVDS_SOLVER

- Tolerance SVDS_TOLERANCE

- Random SeedSVDS_RANDOM_SEED

- Over sampling SVDS_OVER_SAMPLING

- Power Iteration SVDS_POWER_ITERATION

Settings Deprecated:

- Approximate Computation ODMS_APPROXIMATE_COMPUTATION

Support for Explicit Semantic Analysis Algorithm

Oracle Data Miner 17.3 supports a new feature extraction algorithm called Explicit Semantic Analysis algorithm.

The algorithm is supported by two new nodes, that are Explicit Feature Extraction node and Feature Compare node.

- Explicit Feature Extraction Node
The Explicit Feature Extraction node is built using the Explicit Semantic Analysis algorithm.

- Feature Compare Node
The Feature Compare node enables you to perform calculations related to semantics in text data, contained in one Data Source node against another Data Source node.

Explicit Feature Extraction Node

The Explicit Feature Extraction node is built using the Explicit Semantic Analysis algorithm.

You can use the Explicit Feature Extraction node for the following:

- Document classification

- Information retrieval

- Calculations related to semantics

Feature Compare Node

The Feature Compare node enables you to perform calculations related to semantics in text data, contained in one Data Source node against another Data Source node.

The requirements of a Feature Compare node are:

- Two input data sources. The data source can be data flow of records, such as connected by a Data Source node or a single record data entered by user inside the node. In case of data entered by users, input data provider is not needed.

- One input Feature Extraction or Explicit Feature Extraction Model, where a model can be selected for calculations related to semantics.

Enhancement to Data Mining Model Detail View

The model viewers in Oracle Data Miner have been enhanced to reflect the changes in Oracle Data Mining.

Enhancements to the model viewers include the following:

- The computed settings within the model are displayed in the Settings tab of the model viewer.

- The new user embedded transformation dictionary view is integrated with the Inputs tab under Settings.

- The build details data are displayed in the Summary tab under Summary

- The Cluster model viewer detects models with partial details, and displays a message indicating so. This also applies to k-Means model viewer and Expectation Maximization model viewers.

Enhancements to Filter Column Node

Oracle Data mining supports unsupervised Attribute Importance ranking. The Attribute Importance ranking of a column is generated without the need for selecting a target column. The Filter Column node has been enhanced to support unsupervised Attribute Importance ranking.

Mining Model Build Alerts

Oracle Data Miner logs alerts related to model builds in the model viewers and event logs.

After a model build, Oracle Data Miner server queries Oracle Data Mining for any alerts related to the model build. The alerts are logged in:

- Model viewers: The build alerts are displayed in the Alerts tab.

- Event log: All build alerts are displayed along with other details such as job name, node, sub node, time, and message.

R Build Model Node

Oracle Data Mining provides the feature to add R model implementations within the Oracle Data Mining framework. To support R model integration, Oracle Data Miner has been enhanced with a new R Build node with mining functions such as Classification, Regression, Clustering, and Feature Extraction.

Support for Partitioned Models

Oracle Data Miner supports the building and testing of partitioned models.

The following models are enhanced to support partitioned models:

- Build Nodes

- Apply Nodes

- Test Nodes

Oracle Data Miner Features

The new Oracle Data Miner features include:

- Aggregation Node Support for DATE and TIMESTAMP Data Types
The Aggregation node has been enhanced to support DATE and TIMESTAMP data types.

- Enhancement to JSON Query Node
The JSON Query node allows to specify filter conditions on attributes with data types such as ARRAY, BOOLEAN, NUMBER and STRING.

- Enhancement to Build Nodes
All Build nodes are enhanced to support sampling of training data and preparation of numeric data.

- Enhancement to Text Settings
Text settings are enhanced to support the following features:

- Refresh Input Data Definition
Use the Refresh Input Data Definition option if you want to update the workflow with new columns, that are either added or removed.

- Support for Additional Data Types
Oracle Data Miner allows the following data types for input as columns in a Data Source node, and as new computed columns within the workflow:

- Support for In-Memory Column
Oracle Data Miner supports In-Memory Column Store (IM Column Store) in Oracle Database 12.1.0.2 and later, which is an optional static SGA pool that stores copies of tables and partitions in a special columnar format.

- Support for Workflow Scheduling
Oracle Data Miner supports the feature to schedule workflows to run at a definite date and time.

- Enhancement to Polling Performance
Polling performance and resource utilization functionality has been enhanced with new user interfaces.

- Workflow Status Polling Performance Improvement
The performance of workflow status polling has been enhanced.

Aggregation Node Support for DATE and TIMESTAMP Data Types

The Aggregation node has been enhanced to support DATE and TIMESTAMP data types.

For DATE and TIMESTAMP data types, the functions available are COUNT(), COUNT (DISTINCT()), MAX(), MEDIAN(), MIN(), STATS_MODE().

Enhancement to JSON Query Node

The JSON Query node allows to specify filter conditions on attributes with data types such as ARRAY, BOOLEAN, NUMBER and STRING.

The user can apply filters to the data in hierarchical order using the option All or Any in the Filter Settings dialog box. The user also has the option to specify whether to apply filters to data that is used for relational data projection or aggregation definition or both by using any one of the following options:

- JSON Unnest — Applies filter to JSON data that is used for projection to relational data format.

- Aggregations — Applies filters to JSON data that is used for aggregation.

- JSON Unnest and Aggregations — Applies filter to both.

Enhancement to Build Nodes

All Build nodes are enhanced to support sampling of training data and preparation of numeric data.

The enhancement is implemented in the Sampling tab in all Build nodes editors. By default, the Sampling option is set to OFF. When set to ON, the user can specify the sample row size or choose the system determined settings.

Note:
Data preparation is not supported in Association Build model.

The Sampling option is available in the following Build node editors:

- Edit Anomaly Detection Node

- Edit Association Build Node

- Edit Classification Build Node

- Edit Clustering Build Node

- Edit Explicit Feature Extraction Build Node

- Edit Feature Extraction Build Node

- Edit Regression Build Node

Enhancement to Text Settings

Text settings are enhanced to support the following features:

- Text support for synonyms (thesaurus): Text Mining in Oracle Data Miner supports synonyms. By default, no thesaurus is loaded. The user must manually load the default thesaurus provided by Oracle Text or upload his own thesaurus.

- New settings added in Text tab:

- Minimum number of rows (documents) required for a token

- Max number of tokens across all rows (documents)

- New tokens added for BIGRAM setting:

- BIGRAM: Here, NORMAL tokens are mixed with their bigrams

- STEM BIGRAM: Here, STEM tokens are extracted first and then stem bigrams are formed.

Refresh Input Data Definition

Use the Refresh Input Data Definition option if you want to update the workflow with new columns, that are either added or removed.

The Refresh Input Data Definition option is equivalent to SELECT* capability in the input source. The option allows you to quickly refresh your workflow definitions to include or exclude columns, as applicable.

Note:
The Refresh Input Data Definition option is available as a context menu option in Data Source nodes and SQL Query nodes.

Support for Additional Data Types

Oracle Data Miner allows the following data types for input as columns in a Data Source node, and as new computed columns within the workflow:

- RAW

- ROWID

- UROWID

- URITYPE

The URITYPE data type provides many sub type instances, which are also supported by Oracle Data Miner. They are:

- HTTPURITYPE

- DBURITYPE

- XDBURITYPE

Support for In-Memory Column

Oracle Data Miner supports In-Memory Column Store (IM Column Store) in Oracle Database 12.1.0.2 and later, which is an optional static SGA pool that stores copies of tables and partitions in a special columnar format.

Oracle Data Miner has been enhanced to support In-Memory Column in nodes in a workflow. For In-Memory Column settings, the options to set Data Compression Method and Priority Level are available in the Edit Node Performance Settings dialog box.

Support for Workflow Scheduling

Oracle Data Miner supports the feature to schedule workflows to run at a definite date and time.

A scheduled workflow is available only for viewing. The option to cancel a scheduled workflow is available. After cancelling a scheduled workflow, the workflow can be edited and rescheduled.

Enhancement to Polling Performance

Polling performance and resource utilization functionality has been enhanced with new user interfaces.

The enhancement is supported by the following features:

- The repository property POLLING_IDLE_ENABLED is added to determine whether the user interface will use automatic query or manual query for workflow status. This applies to the Workflow Jobs and Scheduled Jobs user interface. However, the Workflow Editor will continue to poll automatically when monitoring a workflow that is running.

Note:
When POLLING_IDLE_ENABLED is set to TRUE, then automatic query for workflow status sets in. When POLLING_IDLE_ENABLED is set to FALSE, then manual query sets in.

A new dockable window Scheduled Workflow has been added that displays the list of scheduled jobs and allows the user to manage the scheduled jobs.

- The Workflow Jobs window is enhanced with the following new features:

- Manual refresh of workflow jobs.

- Administrative override of automatic updates through Oracle Data Miner repository settings.

- Access to Workflow Jobs preferences through the new Settings option.

Workflow Status Polling Performance Improvement

The performance of workflow status polling has been enhanced.

The enhancement includes new repository views, repository properties, and user interface changes:

- The repository view ODMR_USER_WORKFLOW_ALL_POLL is added for workflow status polling.

- The following repository properties are added:

- POLLING_IDLE_RATE: Determines the rate at which the client will poll the database when there are no workflows detected as running.

- POLLING_ACTIVE_RATE: Determines the rate at which the client will poll the database when there are workflows detected running.

- POLLING_IDLE_ENABLED: Determines whether the user interface will use automatic query or manual query for workflow status. This applies to the Workflow Jobs and Scheduled Jobs user interface. However, the Workflow Editor will continue to poll automatically when monitoring a workflow that is running.

Note:
When POLLING_IDLE_ENABLED is set to TRUE, then automatic query for workflow status sets in. When POLLING_IDLE_ENABLED is set to FALSE, then manual query sets in.

- POLLING_COMPLETED_WINDOW: Determines the time required to include completed workfows in the polling query result.

- PURGE_WORKFLOW_SCHEDULER_JOBS: Purges old Oracle Scheduler objects generated by the running of Data Miner workflows.

- PURGE_WORKFLOW_EVENT_LOG: Controls how many workflow runs are preserved for each workflow in the event log. The events of the older workflow are purged to keep within the limit.

- New user interface includes the Scheduled Jobs window which can be accessed from the Data Miner option in both Tools menu and View menu in SQL Developer 17.3.

Oracle Database Features

The new Oracle Database feature includes the support for expanded object name.

The support for schema name, table name, column name, and synonym that are 128 bytes are available in the upcoming Oracle Database release. To support Oracle Database, Oracle Data Miner repository views, tables, XML schema, and PL/SQL packages are enhanced to support 128 bytes names.

1 Oracle Data Miner

Oracle Data Miner is an extension to Oracle SQL Developer. Oracle Data Miner is a graphical user interface to Oracle Data Mining, a feature of Oracle Database.

Data analysts can use the intuitive Oracle Data Miner graphical user interface (GUI) to discover hidden patterns, relationships, and insights in their data. With Oracle Data Miner, everything occurs in an Oracle Database—in a single, secure, scalable platform for advanced business intelligence. Oracle Data Miner eliminates data movement and duplication, maintains security, and minimizes latency time from raw data to valuable information. Enterprises can use Oracle Data Miner for knowledge discovery to better compete on analytics.

Oracle Data Miner helps you perform the data preparation and model building required by the data mining process.

- About the Data Mining Process
Data mining is the process of extracting useful information from masses of data by extracting patterns and trends from the data.

- Overview of Oracle Data Miner
Oracle Data Miner is an extension to Oracle SQL Developer. It is a graphical user interface to Oracle Data Mining, a feature of Oracle Database.

- Architecture of Oracle Data Miner
Oracle Data Miner consists of a server and one or more clients.

- Snippets in Oracle Data Miner
Snippets are code fragments, such as SQL functions, optimizer hints, and miscellaneous PL/SQL programming techniques.

- About Oracle Data Miner Repository Installation
The Oracle Data Miner repository resides in the database that the Oracle Data Miner client connects to. The repository stores metadata about workflows.

- About Dropping the Oracle Data Miner Repository
If you plan to stop using Oracle Data Miner, then you must drop the Repository. You may also have to drop the repository when you upgrade from one version of Oracle Database to another.

- About Oracle Data Miner Repository Migration
Migration may require conversion of the repository. If migration is required, then the Migrate Oracle Data Miner Repository dialog box opens.

- How to Use Oracle Data Miner
Lists the different ways in which you can learn how to use Oracle Data Miner.

About the Data Mining Process

Data mining is the process of extracting useful information from masses of data by extracting patterns and trends from the data.

Data mining requires a problem definition, collection and cleansing of data, and model building. Most of the time spent in a typical data mining project is devoted to understanding and processing of data.

Related Topics

- Oracle Data Mining Concepts

Overview of Oracle Data Miner

Oracle Data Miner is an extension to Oracle SQL Developer. It is a graphical user interface to Oracle Data Mining, a feature of Oracle Database.

- Oracle Data Miner enables users to build descriptive and predictive models to:

- Predict customer behavior

- Target best customers

- Discover customer clusters, segments, and profiles

- Identify customer retention risks

- Identify promising selling opportunities

- Detect anomalous behavior

- Oracle Data Miner provides an Application Programming Interface (API) that enables programmers to build and use models.

- Oracle Data Miner workflows capture and document the analytical methodology of the user. It can be saved and shared with others to automate advanced analytical methodologies.

- The Oracle Data Miner GUI is an extension to Oracle SQL Developer 3.0 or later that enables data analysts to:

- Work directly with data inside the database

- Explore the data graphically

- Build and evaluate multiple data mining models

- Apply Oracle Data Miner models to new data

- Deploy Oracle Data Miner predictions and insights throughout the enterprise

Figure 1-1 shows a sample workflow of Oracle Data Miner.

Figure 1-1 Sample Data Miner Workflow

Oracle Data Miner creates predictive models that application developers can integrate into applications to automate the discovery and distribution of new business intelligence—predictions, patterns, and discoveries—throughout the enterprise.

Related Topics

- Oracle Data Miner Documentation

Architecture of Oracle Data Miner

Oracle Data Miner consists of a server and one or more clients.

Before you install Oracle Data Miner, you must understand the architecture of Oracle Data Miner:

- Oracle Data Miner, the client, is an integrated feature of Oracle SQL Developer 3.0 or later.

- Oracle Database 12c Release 2 (12.2) Enterprise Edition (includes Oracle Database Personal Edition) or an earlier version of Oracle Database Enterprise Edition is the server. In addition to the database, Oracle Data Miner requires the installation of a Data Miner repository account. The repository is a separate account in the database named ODMRSYS. This repository is shared by all user accounts in the database that have been granted the appropriate privileges to use the Data Miner repository.

Figure 1-2 shows the components of Oracle Data Miner.

Figure 1-2 Oracle Data Miner Components

Oracle Database Enterprise Edition includes these services that are critical to the support of Oracle Data Miner:

- Oracle Data Miner: A component of the Oracle Advanced Analytics option to Oracle Database Enterprise Edition. Oracle Data Miner provides model building, testing, and scoring capabilities for Oracle Data Miner.

- Oracle XML DB: Provides services to manage the Oracle Data Miner repository metadata, such as the details about the workflow specifications.

- Oracle Scheduler: Provides the engine for scheduling the Oracle Data Miner workflows.

- Oracle Text: Provides services necessary to support text mining.

Snippets in Oracle Data Miner

Snippets are code fragments, such as SQL functions, optimizer hints, and miscellaneous PL/SQL programming techniques.

SQL Developer provides snippets to help you write PL/SQL programs. Some snippets are just syntax, and others are examples. You can insert and edit snippets when you are using the SQL Worksheet or when creating or editing a PL/SQL function or procedure using SQL Worksheet or SQL Query node.

- Open SQL worksheet: To open SQL Worksheet, go to Connections for SQL Developer, right-click the connection to use, and select Open SQL Worksheet.

- Insert snippet: To insert a snippet into your code in a SQL Worksheet or in a PL/SQL function or procedure, drag the snippet from the snippets window and drop it into the desired place in your code. Then edit the syntax so that the SQL function is valid in the current context. To see a brief description of a SQL function in a tool tip, hold the pointer over the function name. Oracle Data Miner provides snippets for the EXPLAIN, PREDICT, and PROFILE procedures in DBMS_PREDICTIVE_ANALYTICS, and for the Data Mining functions for scoring data using Prediction, Clustering, or Feature Extraction.

- Using Predictive Analytics Snippets
In Oracle SQL Developer, you can view the list of Predictive Analytics snippets and use them.

See Also:
See the SQL Developer Online Help for more information about SQL Worksheet.

Using Predictive Analytics Snippets

In Oracle SQL Developer, you can view the list of Predictive Analytics snippets and use them.

To view the list of Predictive Analytics functions, and to use a snippet:

1. Open Oracle SQL Developer.

2. Select the connection that you are using for Oracle Data Miner.

3. From the SQL Developer menu, go to View and then select Snippets.

4. From the drop-down list, select Predictive Analytics.

The Predictive Analytics group of snippets includes the following snippets:

- Explain: Use DBMS_PREDICTIVE_ANALYTICS.EXPLAIN() to rank attributes in order of influence when explaining a target column.

- Predict: Use DBMS_PREDICTIVE_ANALYTICS.PREDICT() to predict the value of a target column based on values in the input data.

- Prediction Anomaly Function: Use the Anomaly Detection Predictive query to predict anomalous customers.

- Prediction Classification Function: Makes predictions using dynamic classifications.

- Prediction Cluster Function: Predicts the cluster a customer belongs to.

- Prediction Feature Set Function: Predicts feature sets to provide a general characterization of the underlying customer data.

- Prediction Regression Function: Predicts the age of customers who are likely to use an affinity card.

- Profile: Use DBMS_PREDICTIVE_ANALYTICS.PROFILE() to generate rules that identify the records that have the same target value.

5. To use a snippet, drag the snippet to the SQL Worksheet or to a place in a PL/SQL program.

Note:

The Explain, Predict, and Profile snippets have one or more commented-out DROP statements, such as:

--DROP TABLE mining_explain_result;

If you run one of these snippets more than once, then remove the comment characters for the DROP statement.

If you drag the Explain snippet to SQL Worksheet, then you see:

--Available in Oracle Enterprise DB 10.2 and later

--Ranks attributes in order of influence to explain a target column.

--For more info go to: http://www.oracle.com/pls/db112/vbook_subject?subject=dma

--Remove comment on the Drop statement if you want to rerun this script

--DROP TABLE mining_explain_result;

--Perform EXPLAIN operation

BEGIN

DBMS_PREDICTIVE_ANALYTICS.EXPLAIN(

data_table_name => '"CUSTOMERS"',

explain_column_name => '"CUST_GENDER"',

result_table_name => 'mining_explain_result',

data_schema_name => '"SH"');

END;

/

--output first 10 rows from resulting table mining_explain_result

COLUMN ATTRIBUTE_NAME FORMAT A30

COLUMN ATTRIBUTE_SUBNAME FORMAT A30

COLUMN EXPLANATORY_VALUE FORMAT 0D999999

COLUMN RANK FORMAT 999999

select * from mining_explain_result where rownum < 10;

When you run this code, you get the following results (in Script Output):

anonymous block completed

ATTRIBUTE_NAME ATTRIBUTE_SUBNAME EXPLANATORY_VALUE RANK

----------------- ------------------------------ ----------------- ------

CUST_LAST_NAME 0.151359 1

CUST_MARITAL_STATUS 0.015043 3

CUST_INCOME_LEVEL 0.002592 4

CUST_CREDIT_LIMIT 0.000195 5

CUST_EMAIL 0.000000 6

CUST_TOTAL 0.000000 6

CUST_TOTAL_ID 0.000000 6

CUST_FIRST_NAME 0.000000 6

9 rows selected

About Oracle Data Miner Repository Installation

The Oracle Data Miner repository resides in the database that the Oracle Data Miner client connects to. The repository stores metadata about workflows.

The Oracle by Example tutorial Setting Up Oracle Data Miner 4.1 describes how to install the Oracle Data Miner Repository and grants rights to an account. The tutorial describes the following steps:

1. Create a Data Miner User Account using SQL Developer.

2. Create a SQL Developer connection for the Data Miner User.

3. Install the Data Miner Repository.
By default, sample data is installed. You can deselect loading these tables and views.

You can drop the repository through GUI, if necessary.

If you have dropped a Repository or if you do not want to use the OBE, then you must reinstall the repository.

When you install the repository, rights are automatically granted to the user account that you connect with.

To grant rights to another account, define a database connection for the account and open the account in the Data Miner navigator. The GUI tells you that the account does not have the correct grants. Click OK for the grants to be created. You must log in using DBA privilege.

- Installing the Data Miner Repository Using the GUI
The Data Miner Repository installation process starts automatically when you activate a SQL Developer connection for the first time from the Data Miner tab.

Related Topics

- About Dropping the Oracle Data Miner Repository

- Oracle Data Mining 4.1 OBE (Oracle By Example) Series

Installing the Data Miner Repository Using the GUI

The Data Miner Repository installation process starts automatically when you activate a SQL Developer connection for the first time from the Data Miner tab.

To install the repository:

1. Double-click a connection. If the repository is not installed, then the warning message

Repository not Installed
is displayed. You also see this message if you try to create a project when the repository is not installed.

Click Yes to install a Data Miner repository in the database to which you are connected.

Note:

If you have several database connections, then you need to install the repository using only one connection. You must grant privileges to the other connections.

2. Log in using an administrative account for the database that you are connected to.

3. The Repository Installation Settings dialog box opens.

- Name of the repository—ODMRSYS. You cannot change this name.

- Select a default tablespace and a temporary tablespace, and click OK.
If you are connected to Oracle Database 11g Release 2 (11.2.0.4) or later, then permanent tablespaces are filtered so that only Oracle ASM tablespaces are displayed. The temporary tablespace does not have to be Oracle ASM.

If you are connected to Oracle Database 11g Release 2 (11.2.0.3) or earlier, then no filtering takes place.

4. The Install Data Miner Repository dialog box opens. By default, demo data is installed used by the Oracle By Example tutorials. Click Start to begin the installation.

Note:

The sample data requires the SH schema.

5. The installation may take several minutes. After the installation completes, you get a message indicating that the task completed successfully. You can examine the log of the task by clicking Show Log. Click Close when done.

6. If you have more than one Connection, then you must grant privileges to each connection as follows:

a. Click the connection.

b. In the Required Privileges Missing dialog box, click Yes to grant privileges.

c. Log in using an administrative account for the database that you connect to.

d. In the Data Miner Grants dialog box, click Start to grant privileges and to install sample data.

e. After the tasks completes, a message is displayed indicating that the task has completed successfully. You can examine the log of the task by clicking Show Log.

f. Click Close when done.

About Dropping the Oracle Data Miner Repository

If you plan to stop using Oracle Data Miner, then you must drop the Repository. You may also have to drop the repository when you upgrade from one version of Oracle Database to another.

When you drop the Repository, all workflows and internal tables are dropped. Models created by Oracle Data Miner are dropped. Tables created by the Create Table or View node are not dropped.

Note:

Drop Repository cannot be undone. When you drop a repository, the repository and all internal user objects created by Data Miner are permanently removed. The objects removed include models, tables or views generated by the Create Table node, hidden tables used store results (model viewers), and text specification objects used to support text transformations. In addition all Data Miner workflows are dropped. To save a workflow, export it, and later import it.

Before you drop the Repository, check that no projects or workflows are open. If any connections are open, then the process may fail.

For Drop Repository to complete successfully, no sessions with the role of ODMRUSER can be active. Active ODMRUSER sessions can result in object locks that block dropping of the repository.

During the process of dropping the repository:

- The database does not allow any new connections to be established during this process.

- All sessions with the ODMRUSER role are automatically disconnected.

- All workflows and internal tables are dropped. Models created by Oracle Data Miner are dropped. Tables created by the Create Table or View node are dropped.

Note:

After you drop the repository, you must install the repository again before you can perform any data mining.

- Dropping the Data Miner Repository Using GUI
There is one Oracle Data Miner repository per database. If you have several database connections, then it is necessary to drop the repository for one connection only.

Related Topics

- Exporting a Workflow Using the GUI

- Importing a Workflow

Dropping the Data Miner Repository Using GUI

There is one Oracle Data Miner repository per database. If you have several database connections, then it is necessary to drop the repository for one connection only.

To drop the repository:

1. In SQL Developer UI, go to Tools and click Data Miner. Then click Drop Repository.

2. The Drop Repository dialog box opens. Select the connection where you want to drop the repository. You can add and edit connections here, if required.

3. Log in with DBA privilege and click OK.

4. A list of sessions that will be disconnected is displayed. Click OK to continue.

5. The Drop Data Miner Repository dialog box opens. Click Start to begin the process of dropping the repository. This process may take several minutes. Specific messages indicate which steps are being performed.

After the operation completes, examine the log files to see which operations were performed.

About Oracle Data Miner Repository Migration

Migration may require conversion of the repository. If migration is required, then the Migrate Oracle Data Miner Repository dialog box opens.

If you have downloaded a new version of SQL Developer that is not compatible with the installed Data Miner repository, then you will be notified that a Data Miner repository upgrade is necessary when you open a connection used for data mining. The GUI issues a message describing the problem and asks if it should perform necessary migration. If you answer yes, then you are prompted for the administrative password.

- Migrating the Oracle Data Miner Repository Using the GUI
Migration of Oracle Data Miner repository arises when upgrading the Oracle Data Miner client, or when upgrading Oracle Database on which SQL Developer is installed.

Migrating the Oracle Data Miner Repository Using the GUI

Migration of Oracle Data Miner repository arises when upgrading the Oracle Data Miner client, or when upgrading Oracle Database on which SQL Developer is installed.

You must migrate the Oracle Data Miner repository in the following cases:

- A version of SQL Developer before 4.0 is installed on Oracle Database 11g Release 2 (11.2.0.4) or later, and you want to connect using a SQL Developer 4.0 or later client, that is, you upgrade the client.

- SQL Developer 4.0 installed on a version of Oracle Database 11g Release 2 (11.2.0.3) or earlier, and the database is upgraded to Oracle Database 11g Release 2 (11.2.0.4) or later.

Either of these conditions is detected when you open the connection in the Data Miner Navigator. If the ODMRSYS default permanent tablespace is not an ASM tablespace, then a dialog is displayed requesting an ASM tablespace. The ASM tablespace does not replace the existing default permanent tablespace already specified for ODMRSYS, but instead, it converts the workflow_data column that stores the workflow XML data in the ODMR$_WORKFLOWS table. The workflow_data is usually the largest data component stored in ODMRSYS. This approach reduces the amount of time required to perform the migration.

The Repository Migration does not request temporary tablespace.

How to Use Oracle Data Miner

Lists the different ways in which you can learn how to use Oracle Data Miner.

You can learn how to use Oracle Data Miner in the following ways:

- By using Oracle By Example tutorials

- By experimenting on your own using the sample data

- By using the Online Help

- By asking questions in the Oracle Data Mining forum

- By referring to Oracle Data Miner documentation for reference information about data mining in general and Oracle Data Miner in particular.

- Oracle By Example for Oracle Data Miner 4.1
The Oracle By Example tutorials teach you how to install and use Oracle Data Miner.

- Sample Data
Sample Data is loaded in your account when you install Oracle Data Miner.

- Oracle Data Miner Online Help
The online help specific to Oracle Data Miner is in the help folder Oracle Data Miner Concepts and Usage.

- Oracle Data Mining Forum
Oracle Data Mining forum is a discussion forum to share, participate, and follow discussions related to data mining and Oracle Data Miner. You must log in to participate.

- Oracle Data Miner Documentation
Oracle Data Miner is the graphical user interface for Oracle Data Mining. Oracle Data Miner is a component of the Oracle Advanced Analytics option to Oracle Database Enterprise Edition.

Oracle By Example for Oracle Data Miner 4.1

The Oracle By Example tutorials teach you how to install and use Oracle Data Miner.

You can learn how to:

- Set Up Oracle Data Miner 4.1: This tutorial covers the process of setting up Oracle Data Miner for use within Oracle SQL Developer 4.1 connected to Oracle Database 12c.

- Use Oracle Data Miner 4.1: This tutorial covers the use of Oracle Data Miner 4.1 to perform data mining on Oracle Database 12c. In this lesson, you examine and solve a data mining business problem by using the Oracle Data Miner graphical user interface (GUI). The Oracle Data Miner GUI is included as an extension of Oracle SQL Developer, version 4.1.

- Use Feature Selection and Generation with GLM: This tutorial covers the use of Oracle Data Miner 4.1 to leverage enhancements to the Oracle implementation of Generalized Liner Models (GLM) for Oracle Database 12c. These enhancements include support for Feature Selection and Generation.

- Perform Text Mining with an Expectation Maximization Clustering Model: This tutorial covers the use of Oracle Data Miner 4.1 to leverage new text mining enhancements while applying a clustering model. In this lesson, you learn how to use the Expectation Maximization (EM) algorithm in a clustering model.

- Use Predictive Queries With Oracle Data Miner 4.1: This tutorial covers the use of Predictive Queries against mining data by using Oracle Data Miner 4.1.

- Use the SQL Query Node in an Oracle Data Miner Workflow: This tutorial covers the use of the new SQL Query Node in an Oracle Data Miner 4.1 workflow.

Note:

The tutorials are in Oracle Learning Library under the Oracle Data Miner 12c OBE Series.

Related Topics

- Oracle Data Mining 4.1 OBE (Oracle By Example) Series

Sample Data

Sample Data is loaded in your account when you install Oracle Data Miner.

Note:
The SH sample schema is not shipped with Oracle Database 12.2. To install the sample schema, go to DB Sample Schemas.

Related Topics

- DB Sample Schemas

Oracle Data Miner Online Help

The online help specific to Oracle Data Miner is in the help folder Oracle Data Miner Concepts and Usage.

To view or search the online help for Oracle Data Miner click Help and then click Table of Content. Then expand the Table of Content and go to Oracle Data Miner Concepts and Usage on the Contents tab of Help Center.

To get help for a specific dialog box, click the Help button or press F1. To get help for objects in a workflow, select the object and press the F1 key.

Online help contains reference topics and the topics that describe how the GUI works. To see reference topics, either expand the help contents in the online help or search in the online help.

- Search the Online Help

Search the Online Help

To search the online help, use the search box at the top of Help Center:

Type the word or words that you want to search for in the search box and press Enter.

Select one of the following search options: case sensitive (Match case) or case insensitive; and whether to match topics based on all specified words, any specified words, or a Boolean expression.

Search performs a full-text search of all Oracle Data Miner online help topics, including Oracle Data Miner Release Notes.

To cancel a search, click .

Oracle Data Mining Forum

Oracle Data Mining forum is a discussion forum to share, participate, and follow discussions related to data mining and Oracle Data Miner. You must log in to participate.

Participate in the Oracle Data Miner Discussion Forum to discuss about Oracle Data Miner and Oracle Data Mining.

Related Topics

- Oracle Data Mining Forum

Oracle Data Miner Documentation

Oracle Data Miner is the graphical user interface for Oracle Data Mining. Oracle Data Miner is a component of the Oracle Advanced Analytics option to Oracle Database Enterprise Edition.

Oracle Data Miner documentation is included in the Oracle Database Documentation Library for the version of the database that you have installed. Documentation Libraries are posted at the Documentation site in the Database section.

- Oracle Database Documentation

- Oracle Database 11g Release 2 Data Warehousing documentation

- Oracle Database 12c Release 2 Data Warehousing documentation

2 Connections for Data Mining

Oracle Data Mining takes place in an Oracle Database. All data mining objects including input tables and models reside in a database.

This chapter contains the following topics:

- About the Data Miner Tab
The Data Miner tab in SQL Developer allows you to establish connection to a database, and create projects and workflows.

- Creating a Connection
You must create a SQL Developer connection to an Oracle Database for the Data Miner user.

About the Data Miner Tab

The Data Miner tab in SQL Developer allows you to establish connection to a database, and create projects and workflows.

The Data Miner tab lists the following:

- Connections

Note:

Connections are listed both in the Connections tab and in the Data Miner tab.

- Projects

- Workflows

- Prerequisites for Data Mining
Lists the mandatory steps that you must follow before you start data mining.

- Viewing the Data Miner Tab
Sometimes the Data Miner tab may not be visible in the SQL Developer window. You have the options to make the Data Miner tab visible.

Prerequisites for Data Mining

Lists the mandatory steps that you must follow before you start data mining.

Ensure the following:

- Specify a connection to an account in an Oracle Database 11g Release 2 (11.2.0.4) and later. The account that you connect to must have all grants required by Oracle Data Mining.

- Define at least one connection to indicate the account where workflow nodes run.

Viewing the Data Miner Tab

Sometimes the Data Miner tab may not be visible in the SQL Developer window. You have the options to make the Data Miner tab visible.

If the Data Miner tab is not visible in the Oracle SQL Developer window, then:

- Go to Tools and click Data Miner. Then, select Make Visible.

- Go to View and click Data Miner. Then, select Data Miner Connections.

This docks the Data Miner tab in the Oracle SQL Developer window.

Creating a Connection

You must create a SQL Developer connection to an Oracle Database for the Data Miner user.

You can create connections to an Oracle Database in the Connections tab and in the Data Miner tab.

- Creating Connections from the Connections Tab
You must create a SQL Developer connection to an Oracle Database for the Data Miner user. You can create this connection from the Connections tab in SQL Developer.

- Creating Connections from the Data Miner Tab
You must create a SQL Developer connection to an Oracle Database for the Data Miner user. You can create this connection from the Data Miner tab.

- Managing a Connection
After you create a connection, you can manage it from the Data Miner Connections context menu.

- Connecting to a Database
For data mining and data analysis, you must establish a connection to a database.

Creating Connections from the Connections Tab

You must create a SQL Developer connection to an Oracle Database for the Data Miner user. You can create this connection from the Connections tab in SQL Developer.

To create connections from the Connections tab:

1. In Oracle SQL Developer, go to View and click Connections. This docks the Connections pane in the Oracle SQL Developer window.

2. In the Connections pane, right-click Connections and click New Connections. Alternately, you can click to add a new connection.

3. In the New/Select Database Connection dialog box, enter the following for the new connection:

- Connection Name: The name of the connection.

- Password: The Data Miner user password.

- Save Password: Select this option to save your password.

4. In the Oracle tab, provide the following details about the Oracle Database:

- Connection Type

- Role

- Hostname

- Port

- SID: This is the Oracle Service Identifier (SID), a name by which the Oracle Database instance is uniquely identified from other database instance.

- Service Name: Select this option if you are connecting to a database that is installed in a cluster such as Oracle Real Application Clusters.

5. Select one or all of the following options:

- OS Authentication

- Kerberos Authentication

- Proxy Connection

6. To connect to the database, click Connect.

7. To save the connection, click Save.

8. To test the connection, click Test.

Creating Connections from the Data Miner Tab

You must create a SQL Developer connection to an Oracle Database for the Data Miner user. You can create this connection from the Data Miner tab.

To create connections from the Data Miner tab:

1. In the Data Miner tab, right-click Connections. The Select Connection dialog appears.
If the Data Miner tab is not visible, then go Tools and click Data Miner. Then, select Make Visible.

2. To create a new connection, click . The New/Select Database Connection is displayed.

3. In the New/Select Database Connection dialog box, enter the following for the new connection:

- Connection Name: The name of the connection.

- Password: The Data Miner user password.

- Save Password: Select this option to save your password.

4. In the Oracle tab, provide the following details about the Oracle Database:

- Connection Type

- Role

- Hostname

- Port

- SID: This is the Oracle Service Identifier (SID), a name by which the Oracle Database instance is uniquely identified from other database instance.

- Service Name: Select this option if you are connecting to a database that is installed in a cluster such as Oracle Real Application Clusters.

5. Select one or all of the following options:

- OS Authentication

- Kerberos Authentication

- Proxy Connection

6. To connect to the database, click Connect.

7. To save the connection, click Save.

8. To test the connection, click Test.

Related Topics

- Creating Connections from the Connections Tab

Managing a Connection

After you create a connection, you can manage it from the Data Miner Connections context menu.

You can perform the following tasks by right-clicking the connection name in the Data Miner tab, and click:

- Disconnect: To disconnect the connection from the database.

Note:

The connection still exists. You can reconnect by double-clicking the connection, and entering the password.

- Add Connection: To create a new connection.

- New Project:To create a new project.

- Remove:To remove a connection.

- Properties: To open the New/Select Database Connection dialog box. You can view and edit connection properties here.

- Viewing and Editing Connection Properties

- Removing a Connection

Related Topics

- Creating Connections from the Connections Tab

- Creating Connections from the Data Miner Tab

- Creating a Project

Viewing and Editing Connection Properties

To view connection properties:

1. In the Data Miner tab, right-click the connection name and select Properties.

2. In the New/Select Database Connection dialog box, you can view the connection properties and edit them, as required.

3. Click OK.

Removing a Connection

To remove a connection:

1. In the Data Miner tab, right-click the connection name and select Remove. This removes the connections from the Data Miner tab.

Note:

Removing the connection from the Data Miner tab does not remove the connection permanently. To permanently remove the connection, delete it from Connections tab.

2. Go to the Connections tab and right-click the connection.

3. Click Delete. This permanently removes the connection.

Note:

When you delete a connection, the projects and workflows accessible through the connection are not deleted.

Connecting to a Database

For data mining and data analysis, you must establish a connection to a database.

To connect to Oracle Database:

1. In the Data Miner tab, right-click the connection that you want to connect and click Add Connections. If the Data Miner tab is not visible, then dock it in the Oracle SQL Developer window.

2. In the Select Connections dialog box, select the connection from the Connections drop-down list. If you are disconnected from the connection, then you may have to provide a password.

3. Click OK.

- Provide Password

Related Topics

- Viewing the Data Miner Tab

Provide Password

If you do not select the Save Password option when defining the connection, then you must provide the password when you connect.

After you log in, Oracle Data Miner monitors the connection. You can now perform data mining operations.

3 Data Miner Projects

A Data Miner project resides in a database connection.

The project contains the workflows created for the project. You must create at least one project before you create any workflow.

This chapter contains the following topics:

- Creating a Project
You must create at least one project in a connection. You can create multiple projects in a connection.

- Managing a Project
The options to manage a Data Miner project are available in the context menu.

Creating a Project

You must create at least one project in a connection. You can create multiple projects in a connection.

To create a project:

1. Connect to a database.

2. In the Data Miner tab, expand Connections and right-click the connection in which you want to create the project. Click New Project.

Note:

If the Data Miner tab is not visible, then you can dock it in the Oracle SQL Developer window.

3. In the New Project dialog box, enter the following details:

- Project Name: Enter a project name. Ensure that it conforms to the project naming conventions.

- Comment: You can enter any comment in 4000 or fewer characters. This is an optional field.

4. Click OK.

After the project is created, you can rename it, edit any comments, delete it, or copy it to an other connection.

- Project Name Restrictions
Data Miner project names must meet certain conditions.

Related Topics

- Managing a Project

- Viewing the Data Miner Tab

- Connecting to a Database

Project Name Restrictions

Data Miner project names must meet certain conditions.

The conditions are:

- The name must be unique within the project.

- The character count for the workflow name should be between 1 and 128.

- The name cannot contain a slash (/).

Managing a Project

The options to manage a Data Miner project are available in the context menu.

In the Data Miner tab, right-click the project that you want to manage. The following operations are available to manage the project:

- New Workflow: To create a new workflow.

- New Project: To create a new project.

- Delete: To delete a project.

- Properties: To add or modify comments for the project. You can edit the comments for an existing project from here.

- Rename: To rename the project.

- Import Workflow: To import a workflow in to this project, that was previously exported.

- Deleting a Project

- Expanding a Project

- Project Properties

- Rename Project

- Importing a Workflow

Deleting a Project

To delete a project, right-click the project and click Delete. The following are deleted when you delete a project:

- All workflows contained in the project.

- All objects generated by those workflows such as models or apply results.

The database objects, such as tables referenced by workflows in the project, are not removed when a project is deleted.

Note:

You cannot undo a project deletion.

Expanding a Project

To expand a project:

- Click the plus sign to the left of the project name

- Double-click the project name

The list of workflows created in the project is displayed.

Project Properties

You can add and edit information related to the project in the Comment field. After editing, click OK To

Rename Project

To rename a project:

1. Right-click the project that you want to rename. The Rename Project dialog box opens.

2. In the Rename To field, enter the name of the project.

3. Click OK.

Importing a Workflow

You can import predefined workflows into a project. Before you import a workflow, ensure that the import requirements of a workflow are met.

To import a workflow:

1. In the Data Miner tab, right-click the project where you want to import the workflow and click Import Workflow. The Open dialog box opens.

2. In the Open dialog box, navigate to the location where the workflow XML file is.

3. Click Open. The system verifies that the specified file contains a workflow and determines the version number of the workflow. The Import Workflow dialog box opens. You can select and import only one workflow at a time.

4. In the Import Workflow dialog box, specify a name for the new workflow and select any one of the following options to handle naming conflicts:

- Rename Model and Output Table name if necessary

- Keep Existing Model and Output Table name even if conflict exists

- Import Workflow
The Import Workflow dialog box enables you to specify the name of the workflow that is imported and how to handle naming conflicts.

Related Topics

- Import Requirements of a Workflow

Import Workflow

The Import Workflow dialog box enables you to specify the name of the workflow that is imported and how to handle naming conflicts.

The default workflow name is the file name of the workflow. It may be necessary to:

- Change the name slightly to avoid conflicts.

- Edit the name so that it is a valid workflow name. The name of the new workflow must be unique in the new connection.

You must also select how to handle naming conflicts in names of workflow nodes and output tables. The default is Rename Model and Output Table names if necessary.

You can select Keep Existing Model and Output table names even if conflict exists. If you select this option, then you must resolve any conflicts yourself.

To start the import, click OK.

4 Workflow

A Data Miner workflow is a mechanism to define data mining operations such as model build, test, and apply.

These operations are defined in nodes, which comprise the workflow. You can create multiple workflows in a single project.

- About Workflows
A workflow must contain one or more sources of data, such as a table or a model.

- Working with Workflows
A workflow allows you to create a series of nodes, and link them to each other to perform the required processing on your data. You cannot use Oracle Data Miner until you have created a workflow for your work.

- About Nodes
Nodes define the data mining operations in a workflow.

- Working with Nodes
You can perform the following tasks with any nodes.

- About Parallel Processing
In Parallel Query or Parallel Processing, multiple processes work simultaneously to run a single SQL statement.

- Setting Parallel Processing for a Node or Workflow
By default, parallel processing is set to OFF for any node type.

- About Oracle Database In-Memory
The In-Memory Column store (IM column store) is an optional, static System Global Area (SGA) pool that stores copies of tables and partitions in a special columnar format in Oracle Database 12c Release 1 (12.1.0.2) and later.

About Workflows

A workflow must contain one or more sources of data, such as a table or a model.

For example, to build a Naive Bayes model, you must first identify the input with a Data Source node. Then you create a Classification node to build and test the model.

By using a workflow, you can:

- Build, analyze, and test data mining process

- Identify and examine data sources

- Build and apply models

- Create predictive queries

- Workflow Sequence
A workflow is built and run in a certain sequence.

- Workflow Terminology
A workflow is a directed graph consisting of nodes that are connected to one another.

- Workflow Thumbnail
The thumbnail view of the workflow provides an overall view of the workflow. Thumbnails are most useful for large workflows.

- Components
The Components pane lists the components that you can add to workflows.

- Workflow Properties
The Workflow Properties tab enables you to add or change comments associated with the selected workflow.

- Properties
Properties enables you to view and change information about the entire workflow or a node in a workflow.

Workflow Sequence

A workflow is built and run in a certain sequence.

The sequence is:

1. Create a blank workflow.

2. Add nodes to the workflow.

3. Connect the nodes in a workflow.

4. Run the nodes.

5. Examine the results.

6. Repeat the steps as required.

Related Topics

- Creating a Workflow

- Add Nodes or Create Nodes

- Link Nodes

Workflow Terminology

A workflow is a directed graph consisting of nodes that are connected to one another.

There are specific terms used to indicate the nodes in relation to the workflow. For example, consider a workflow composed of two nodes, N1 and N2. Assume that N1 is connected to N2:

- Parent node and child node: The node N1 is the parent node of N2, and the node N2 is a child of N1. A parent node provides information that the child node needs when it runs. You must build a model before you apply to new data.

- Descendants and ancestors: The node N2 is called the descendant of N1 if there is a workflow connection starting from N1 that eventually connects to N2. N1 is called the ancestor of N2. N1 is always closer to the root node than N2.

- Root nodes: The nodes that have no parent nodes are called root nodes. All workflows have at least one root node. A parent node can have multiple root nodes.

Note:

A parent node is closer to a root node than its child node.

- Siblings: If a node has several child nodes, then the child nodes are referred to as siblings.

- Upstream: Parent nodes are called upstream of child nodes.

Workflow Thumbnail

The thumbnail view of the workflow provides an overall view of the workflow. Thumbnails are most useful for large workflows.

To open the thumbnail viewer, go to View and click Thumbnail. Alternately, press Ctrl+Shift+T to open the Thumbnail viewer.

In a Thumbnail view, you can:

- Navigate to a specific node in the workflow

- Change the zoom level of the workflow

- Change the node that is currently viewed in the workflow

The Thumbnail view includes a rectangle. Click inside the viewer to move the rectangle around, thereby changing the focus of the display in the workflow editor. You can also resize the rectangle for a finer level of control.

When viewing models in the thumbnail view, if the model generates trees, then the thumbnail view automatically opens to provide an overall view to display the shape of the tree. The thumbnail view also shows your location in the overall model view.

Components

The Components pane lists the components that you can add to workflows.

To add a component to a workflow, drag and drop the component from the Components pane to the workflow.

The Components pane opens automatically when you load a workflow. If the Components pane is not visible, then go to View and click Components.

The Components pane includes the following:

- My Components: Contains the following two tabs:

- Favorites

- Recently Used

- Workflow Editor: Contains the nodes categorized into categories from where you can use them to create a workflow.

- All Pages: Lists all the available components in one list.

- Workflow Editor

Workflow Editor

The Workflow Editor contains nodes that are categorized into sections. You can create and connect the following nodes in a workflow:

- Data Nodes

- Transforms Nodes

- Model Nodes

- Model Operations

- Predictive Query Nodes

- Text Nodes

- Link Nodes

Workflow Properties

The Workflow Properties tab enables you to add or change comments associated with the selected workflow.

Related Topics

- Properties

Properties

Properties enables you to view and change information about the entire workflow or a node in a workflow.

To view the properties of a node, right-click the node and select Go to Properties.

If you select either the workflow or one of its node, then the respective properties are displayed in the Properties pane. For example, if you select a Data Source node, then the Data Source node properties are displayed in the Properties pane.

Note:

In the earlier releases of Oracle Data Miner, the Properties tab was known as the Property Inspector.

You can perform multiple tasks for a node and a workflow from:

- Properties context menu

- Properties pane

Related Topics

- Performing Tasks from Workflow Context Menu

- Managing Workflows and Nodes in the Properties Pane

Working with Workflows

A workflow allows you to create a series of nodes, and link them to each other to perform the required processing on your data. You cannot use Oracle Data Miner until you have created a workflow for your work.

Ensure that you meet the workflow requirements. You can perform the following tasks with a workflow:

- Creating a Workflow
You must create a workflow to define data mining operations such as model build, test, and apply.

- Deploying Workflows
Oracle Data Miner enables you to generate a script from a workflow that recreates all the objects generated by that workflow.

- Deleting a Workflow
You can delete a workflow from the workflow context menu.

- Loading a Workflow
When you open a workflow, it loads in the workflow pane.

- Managing a Workflow
After you create a workflow, the workflow is listed under Projects in the Data Miner tab.

- Oracle Enterprise Manager Jobs
Oracle Enterprise Manager (OEM) allows database administrators to define jobs through the OEM application.

- Renaming a Workflow
You can rename a workflow using the workflow content menu.

- Runtime Requirements
You must have the Data Miner repository installed on the system where the scripts are run.

- Running a Workflow
The View Event Log enables you to view the progress of a workflow that is running.

- Scheduling a Workflow
Using the Workflow Schedule, you can define a schedule to run a workflow at a definite time and date.

- Workflow Prerequisites
Before you perform any task with a workflow, the workflow prerequisites must be met.

- Workflow Script Requirements
Workflow script requirements include the following:

Creating a Workflow

You must create a workflow to define data mining operations such as model build, test, and apply.

Before you create a workflow, ensure that you meet the workflow prerequisite conditions.

To create a blank workflow:

1. In the Data Miner tab, expand Connections in the left pane.

If the Data Miner tab is not open, then click Tools and select Data Miner.

2. Select the project under which you want to create the workflow.

3. Right-click the project and select New Workflow from the context menu. The Create Workflow dialog box opens.

4. In the Name field, enter a unique name for the workflow.

5. Click OK. This creates a blank workflow.

- Workflow Name Restrictions

Workflow Name Restrictions

A workflow name must meet the following conditions:

- The character count for the workflow name should be between 1 and 128.

- The workflow name must not have a slash (/).

- The workflow name must be unique within the project.

Deploying Workflows

Oracle Data Miner enables you to generate a script from a workflow that recreates all the objects generated by that workflow.

In earlier releases of Data Miner, you could only generate a script for Transformation nodes.

A script that recreates all objects generated by that workflow, also enables you to replicate the behavior of the entire workflow. Such scripts provide a basis for application integration or a lightweight deployment of the workflow, that does not require Data Miner repository installation and workflows in the target and production system.

Oracle Data Miner provides the following two types of deployment:

- Deploy Workflows using Data Query Scripts

- Deploy Workflows using Object Generation Scripts

- Running Workflow Scripts

Deploy Workflows using Data Query Scripts

Any node that generates data has the Save SQL option in its context menu.

The Save SQL option generates a SQL script. The generated SQL can be created using the SQL*Plus script or as a standard SQL script. The advantage of the script format is the ability to override table and model references with parameters.

The Save SQL option enables you to select the kind of script to generate, and the location to save the script.

Related Topics

- Save SQL

Deploy Workflows using Object Generation Scripts

The Object Generation Script generates scripts that create objects. The scripts that generate objects are:

- Scripts Generated

- Script Variable Definitions

Related Topics

- Scripts Generated

- Script Variable Definitions

Running Workflow Scripts

You can run workflow scripts using Oracle Enterprise Manager Jobs or Oracle Scheduler Jobs. You can run the generated scripts in the following ways:

- As Oracle Enterprise Manager Jobs, either as SQL Script or an operating system command.

- As Oracle Scheduler Jobs:

- External Jobs that calls the SQL Script

- Environment: Using Oracle Enterprise Manager for runtime management or using PL/SQL

- Run the scripts using SQL*PLus or SQL Worksheet.

Note:
To run a script, ensure that Oracle Data Miner repository is installed.

Related Topics

- Running Scripts using SQL*Plus or SQL Worksheet

- Oracle Enterprise Manager Jobs

- Runtime Requirements

Deleting a Workflow

You can delete a workflow from the workflow context menu.

To delete a workflow:

1. In the Data Miner tab, expand Projects and select the workflow that you want to delete.

2. Right-click and click Delete.

Loading a Workflow

When you open a workflow, it loads in the workflow pane.

After creating a workflow, you can perform the following tasks:

- Load a workflow: In the Data Miner tab, expand the project and double-click the workflow name. The workflow opens in a new tab.

- Close a workflow: Close the tab in which the workflow is loaded and displayed.

- Save a workflow: Go to File and click Save. If a workflow has any changes, then they are saved when you exit.

Managing a Workflow

After you create a workflow, the workflow is listed under Projects in the Data Miner tab.

To perform any one of the following tasks, right-click the workflow under Projects and select an option from the context menu:

- New Workflow: To create a new workflow in the current project.

- Delete: To delete the workflow from the project.

- Rename: To rename the workflow.

- Export: To export a workflow.

- Import: To import a workflow.

- Exporting a Workflow Using the GUI
You can export a workflow, save it as an XML file, and then import it into another project.

- Import Requirements of a Workflow
To import a workflow, you must meet the requirements related to workflow compatibility, permissions, user account related rights.

- Data Table Names
The account from which the workflow is exported is encoded in the exported workflow.

- Workflow Compatibility
Before you import or export a workflow, ensure that Oracle Data Miner versions are compatible.

- Building and Modifying Workflows
You can build and modify workflows using the Workflow Editor. The Workflow Editor is the tool to modify workflows.

- Missing Tables or Views
If Oracle Data Miner detects that some tables or views are missing from the imported schema, then it gives you the option to select another table.

- Managing Workflows using Workflow Controls
Several controls are available as icon in the top border of a workflow, just below the name of the workflow.

- Managing Workflows and Nodes in the Properties Pane
In the Properties pane, you can view and change information about the entire workflow or a node in a workflow.

- Performing Tasks from Workflow Context Menu
The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

Related Topics

- Creating a Workflow

- Exporting a Workflow Using the GUI

- Importing a Workflow

Exporting a Workflow Using the GUI

You can export a workflow, save it as an XML file, and then import it into another project.

Ensure that the Oracle Data Miner versions, the one from which you export the workflow and the one into which you import the workflow, are compatible.

To export a workflow:

1. In the Data Miner tab, expand Connection and click the project under which the workflow is created.

2. Right-click the workflow that you want to export and click Export. The Save dialog box opens.

3. In the Save dialog box, navigate to the location where you want to export the workflow and click Save. The workflow is saved as an XML file.

Note:

The default directory to save the workflow is the system default directory for saving documents. You can change this directory.

Related Topics

- Workflow Compatibility

Import Requirements of a Workflow

To import a workflow, you must meet the requirements related to workflow compatibility, permissions, user account related rights.

The import requirements of a workflow are:

- All the tables and views used as data sources in the exported workflow must be in the new account.

- The tables or views must have the same name in the new account as they did in the old account.

- It may be necessary to redefine the Data Source node.

- If the workflow includes Model nodes, then the account where the workflow is imported must contain all models that are used. The Model node may have to be redefined in the same way that Data Source nodes are.

- You must have permission to run the workflow.

- The workflow must satisfy the compatibility requirements.

Related Topics

- Data Table Names

- Workflow Compatibility

Data Table Names

The account from which the workflow is exported is encoded in the exported workflow.

Assume that a workflow is exported from the account DMUSER and contains the Data Source node with data MINING_DATA_BUILD.

If you import the schema into a different account, that is, an account that is not DMUSER, and try to run the workflow, then the Data Source node fails because the workflow looks for DMUSER.MINING_DATA_BUILD_V.

To resolve this issue:

1. Right-click the Data Source node MINING_DATA_BUILD_V and select Define Data Wizard.
A message appears indicating that DMUSER.MINING_DATA_BUILD_V does not exist in the available tables or views.

2. Click OK and select MINING_DATA_BUILD_V in the current account. This resolves the issue.

Workflow Compatibility

Before you import or export a workflow, ensure that Oracle Data Miner versions are compatible.

The compatibility requirements for importing and exporting workflows are:

- Workflows exported using a version of Oracle Data Miner earlier than Oracle Database 11g Release 2 (11.2.0.1.2) cannot always be imported into a later version of Oracle Data Miner.

- Workflows exported from an earlier version of Oracle Data Miner may contain invalid XML. If a workflow XML file contains invalid XML, then the import is terminated.

To check the version of Oracle Data Miner:

1. Go to Help and click Version.

2. Click the Extensions tab.

3. Select Data Miner.

Building and Modifying Workflows

You can build and modify workflows using the Workflow Editor. The Workflow Editor is the tool to modify workflows.

The Workflow Editor is supported by:

- Properties

- Components

- Workflow Thumbnail

Missing Tables or Views

If Oracle Data Miner detects that some tables or views are missing from the imported schema, then it gives you the option to select another table.

The schema that is imported into a workflow may not have all the tables or views used by the workflow. When Data Miner detects this problem, it generates the message

Table Selection Failure
indicating that the table is missing. You have the choice to select another table.

To select another table, click Yes. The Define Data Source Wizard opens. Use the wizard to select a table and attributes.

Managing Workflows using Workflow Controls

Several controls are available as icon in the top border of a workflow, just below the name of the workflow.

You can perform the following tasks:

- Zoom in and zoom out workflow nodes: Click the and icon respectively.

- Control node size: Click the percent drop-down list and select the size. Default size is 100%.

- View event log: Click the icon.

- Run selected nodes: Click the icon. When you select the node to be run, the triangle turns green.

- Schedule workflow: Click to create or edit a workflow schedule.

- Refresh Workflow Data Definition: Click to ensure that the workflow is updated with new columns that are either added or removed.

Note:
The Refresh Workflow Data Definition option is applicable only to Data Source node and SQL Query node.

- Set performance settings: Click to set the In-Memory and Parallel settings for the nodes in the workflow.

Managing Workflows and Nodes in the Properties Pane

In the Properties pane, you can view and change information about the entire workflow or a node in a workflow.

To view the properties of a node:

1. Right-click the node and select Go to Properties. The corresponding Properties pane opens. For example, if you select a Data Source Node, then in the Properties pane, the details of the Data Source node is displayed.

2. Use the Search field to find items in Properties.

3. Click the icon to open the editor for the item that you are viewing.

4. Use the options in the context menu to perform additional tasks.

Related Topics

- Performing Tasks from Workflow Context Menu

Performing Tasks from Workflow Context Menu

The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

To view a workflow, double-click the name of the workflow in the Data Miner tab. The workflow opens in a tab located between the Data Miner tab and the Components pane. If you open several workflows, then each workflow opens in a different tab. Only one workflow is active at a time.

The following options are available in the context menu:

- Close: Closes the selected tab.

- Close All: Closes all workflow tabs.

- Close Other: Closes all tabs except the current one.

- Maximize: Maximizes the workflow. The Data Miner tab, Components pane, and other items are not visible. To return to previous size, click the selection again.

- Minimize: Minimizes the Properties tab to a menu. To return to the previous size, right-click the Properties tab, and click Dock.

- Split Vertically: Splits the active editor or viewer into two documents. The two documents are split vertically. Click the selection again to undo the split.

- Split Horizontally: Splits the active editor or viewer into two documents. The two documents are split horizontally. Click the selection again to undo the split.

- New Document Tab Group: Adds the currently active editor or viewer into its own tab group. Use Collapse Editor Tab Groups to undo this operation.

- Collapse Document Tab Groups: Collapses all the editors or viewers into one tab group. Only displayed after New Editor Tab Group.

- Float: Converts the Properties tab into a movable pane.

- Dock: Sets the location for floating window. Alternately, you can press Alt+Shift+D.

- Clone: Creates a new instance of Properties.

- Freeze Content: Freezes the content. To unfreeze Properties, click again on this selection.

Oracle Enterprise Manager Jobs

Oracle Enterprise Manager (OEM) allows database administrators to define jobs through the OEM application.

The job is run through OEM instead of Oracle Scheduler. The job can be run manually from OEM. The running of the job can be monitored. The result is either a success or a reported failure.

- The job definitions can directly open the generated scripts files.

- The job definition should define the master script invocation as a script file using a full file path.

- The job can be run on a schedule or on demand.

- All script that run within the master script must have fully qualified path names.

For information about Enterprise Manager, see the Oracle Enterprise Manager documentation set at Oracle Database 2 Day DBA for the database to which you are connected.

Renaming a Workflow

You can rename a workflow using the workflow content menu.

To change the name of a workflow or project:

1. Right-click the name of the workflow or project in the Data Miner tab and select Rename.

2. The Rename Workflow dialog box or Rename Projectdialog box opens. Enter the new name in the Rename To field.

For a project, the new name must satisfy the project name restrictions. For a workflow the new name must satisfy the workflow name restrictions.

3. Click OK.

Related Topics

- Project Name Restrictions

- Workflow Name Restrictions

Runtime Requirements

You must have the Data Miner repository installed on the system where the scripts are run.

The generated scripts require access to Data Miner repository objects. The script checks the repository version and ensures that the repository is the same version or greater than the version of the source system.

Running a Workflow

The View Event Log enables you to view the progress of a workflow that is running.

You can also view the time taken for workflow creation.

Model builds can be very resource-intensive. The MAX_NUM_THREADS parameter in the Oracle Data Miner server controls the number of parallel builds. MAX_NUM_THREADS specifies the maximum number of model builds across all workflows running in the server.

Default Value=10. Therefore, by default, 10 models can occur concurrently across all workflows. There is the MAX_NUM_THREADS parameter in the repository table ODMRSYS. ODMR$REPOSITORY_PROPERTIES, where you can specify the value.

If you increase the value of MAX_NUM_THREADS, then do it gradually. Workflows can appear to be running even though the network connection is lost.

To control the parallel model build behavior, the following parameters are used:

- THREAD_WAIT_TIME. The default is 5. When MAX_NUM_THREADS is reached, further Build process will be put on queue until parallel model build count MAX_NUM_THREADS. This setting (in seconds) determines how often to check for parallel model build count.

- MAX_THREAD_WAIT. The default is NULL. The timeout (in seconds) for Build process that has been put on queue. If NULL, then no timeout will occur.

- Network Connection Interruption
You may encounter interruption in network connection while running a workflow.

- Locking and Unlocking Workflows
A workflow is locked when it is opened, or one or all its nodes are running.

Related Topics

- View an Event Log

Network Connection Interruption

You may encounter interruption in network connection while running a workflow.

If a network connection to the Data Miner server is interrupted while running a workflow, then the Workflow Editor may indicate that the workflow is still running indefinitely. On the other hand, the Workflow Jobs window may indicate that the connection is down.

Oracle Data Miner issues a message indicating that the connection was lost. If the connection is not recovered in a reasonable period, then close and open the Workflow Editor again.

Note:

Use the Workflow Jobs window to monitor connections.

Locking and Unlocking Workflows

A workflow is locked when it is opened, or one or all its nodes are running.

A workflow is locked under the following conditions:

- When a node is running, the workflow is locked, and none of its nodes can be edited. Existing results may be viewed while the workflow is running. You can also go to a different workflow and edit or run it.

- When a user opens a workflow, the workflow is locked so that other users cannot modify it.

- When a workflow is running, an animation in the tool bar (circular arrow loop) shows that the workflow is running.

- When you open a locked workflow, Locked is displayed in the tool bar and a running indicator if the workflow is running:

- If no one else has the workflow locked, and you are given the lock, then lock icon is removed from the tool bar.

- Unlocking a locked workflow: If a workflow seems to complete, but is still locked, then click Locked to unlock the workflow.

- Refreshing Workflow: Once a locked workflow has stopped running, you can refresh the workflow by clicking the icon. You can also try to obtain the lock yourself by clicking on the lock.

Scheduling a Workflow

Using the Workflow Schedule, you can define a schedule to run a workflow at a definite time and date.

You can also edit an existing Workflow Schedule and cancel any scheduled workflows. To create a workflow schedule:

1. Click the arrow next to the icon.

2. Select an option from the drop-down list:

- Click Create Schedule: to create a schedule. The Create Schedule dialog box opens, where you can create a schedule for:

- All Nodes

- Selected Nodes: Select the nodes for which you want to create the schedule.

- Selected Nodes and Parents: Select the nodes for which you want to create the schedule.

- Selected Nodes and Children: Select the nodes for which you want to create the schedule.

- Children of Selected Nodes only: Select the nodes

- Edit Schedule

- Cancel Schedule

- Create Schedule
Use the Create Schedule option to define a schedule to run a workflow at a definite time and date.

- Repeat

- Repeat Hourly

- Repeat Daily

- Repeat Weekly

- Repeat Monthly

- Repeat Yearly

- Schedule

- Save a Schedule

- Advanced Settings

Create Schedule

Use the Create Schedule option to define a schedule to run a workflow at a definite time and date.

In the Create Schedule dialog box, you can create schedules for your workflows. To create workflow schedule:

1. Start Date: Select a date to set as the start date of the schedule. Click to select a date.

2. Repeat: Select any one of the following options:

- None: To schedule the workflow to run only once at the defined time.

- Every Day: To schedule the workflow to run daily at the specified time.

- Every Week: To schedule the workflow to run weekly at the specified time.

- Custom: To customize your workflow schedule, click Custom. This opens the Repeat dialog box, where you can set how frequently the workflow should run.

3. End Repeat: You can select any one of the following options:

- None: To continue running the workflow every hour.

- After: Select a number by clicking the arrows. This runs the workflow every hour, and would stop after the number of hours you have selected here. For example, if you select 8, then the workflow will run every hour, and after 8 hours, it will stop.

- On Date: Select a particular date by clicking the calendar icon.

4. Select Use Existing Schedule, and select a schedule from the drop-down list if you want to schedule the workflow as per the selected schedule.

- Click to edit the selected schedule in the Schedule dialog box.

- Click to add a new schedule. You can also edit the selected schedule, and add it here.

- Click to delete the selected schedule.

5. Click OK.

To save the workflow schedule settings, click . You can provide a name for the schedule in the Save a Schedule dialog box.

Related Topics

- Repeat

- Save a Schedule

Repeat

In the Repeat dialog box, you can set how frequently the workflow scheduler should run. To set the repeat frequency:

1. Frequency: Select an option to set how frequently your workflow should run.

- Repeat Hourly

- Repeat Daily

- Repeat Weekly

- Repeat Monthly

- Repeat Yearly

2. Every: The values in this field depends on the option that you select in the Frequency field. For example, if you select 2 in the Every field and Hourly in the Frequency field, then the workflow will run every 2 hours. If you select 2 in the Every field and Daily in the Frequency field, then the workflow will run every 2 days. Select an option as applicable.

3. Click OK.

Repeat Hourly

If you select Hourly in the Frequency field, then select after how many hours, the workflow should run.

1. The Frequency field displays Hourly.

2. In Every field select a number by clicking the arrow. This number determines after how many hours the workflow should run. For example, if you select 5, then after every 5 hours, your workflow will run.

3. Click OK. This takes you to the Create Schedule dialog box.

Repeat Daily

If you select Daily in the Frequency field, then select after how many days, the workflow should run.

1. The Frequency field displays Daily.

2. In Every field select a number by clicking the arrow. This number determines after how many days the workflow should run. For example, if you select 5, then after every 5 days, your workflow will run.

3. Click OK. This takes you to the Create Schedule dialog box.

Repeat Weekly

If you select Weekly in the Frequency field, then select after how many weeks, and on which days of the week, the workflow should run.

1. The Frequency field displays Weekly.

2. In Every field, select a number by clicking the arrow. This number determines after how many weeks the workflow should run. For example, if you select 2, then after every 2 weeks, your workflow will run.

3. Select the days of the week on which you want to run the workflow.

4. Click OK. This takes you to the Create Schedule dialog box.

Repeat Monthly

If you select Monthly in the Frequency field, then select after how many months, and on which dates of the month, the workflow should run.

1. The Frequencyfield displays Monthly.

2. In Every field, select a number by clicking the arrow. This number determines after how many months the workflow should run. For example, if you select 2, then every 2 months, your workflow will run.

3. In the Days of Month section, select either of the following options:

- Each: To select a date on which you want your workflow to run. For example, if you select 26, then on the 26th of every month, the workflow will run.

- On the: To select on which days of the month you want to run your workflow. For example, if you select First and Monday, from the two drop-down lists respectively, then on every first Monday of the month, your workflow will run.

4. Click OK. This takes you to the Create Schedule dialog box.

Repeat Yearly

If you select Yearly in the Frequency field, then select after how many years, the workflow should run. Select the months in the appropriate field.

1. The Frequency field displays Yearly.

2. In Every field, select a number by clicking the arrow. This number determines after how many years the workflow should run.

3. Select the months of the year, on which the workflow should run.

4. Click OK. This takes you to the Create Schedule dialog box.

Schedule

In the Schedule dialog box, you can edit workflow schedule. To edit selected workflow schedule:

1. In the Name field, the name of the selected workflow schedule is displayed. This is a non-editable field.

2. In the Start Date field, click to select a different date.

3. In the Repeat field, select a different repeat option. To customize, select Custom and make necessary edits in the Repeat dialog box.

4. In the End Repeat field, you may select any one of the following options:

- Never: To continue running the workflow schedule indefinitely.

- On Date: Click to select a particular date on which to end the running of the workflow.

5. Click OK.

Save a Schedule

The Save a Schedule dialog box allows you to save the workflow schedule. To save a workflow schedule:

1. In the Name field, provide a name for the workflow schedule.

2. Click OK.

Advanced Settings

In the Advanced Settings dialog box, you can set up email notifications, settings related to workflow jobs and nodes. To set up email notifications, and other settings:

- In the Notification tab:

1. Select Enable Email Notification to receive notifications.

2. In the Recipients field, enter the email addresses to receive notifications.

3. In the Subject field, enter an appropriate subject.

4. In the Comments fields, enter comments, if any.

5. Select one or more events for which you want to receive the notifications:

- Started: To receive notifications for all jobs that started.

- Succeeded: To receive notifications for all jobs that succeeded.

- Failed: To receive notifications for all jobs that failed.

- Stopped: To receive notifications for all jobs that stopped.

6. Click OK.

- In the Settings tab:

1. In the Time Zone field, select a time zone of your preference.

2. In the Job Priority field, set the priority of the workflow job by placing the pointer between High and Low.

3. Select Max Failure and set a number as the maximum number of failed workflow execution.

4. Select Max Run Duration and set the days, hours and minutes for the duration of maximum run time of the workflow job.

5. Select Schedule Limit and set the days, hours and minutes.

6. Click OK.

- In the Nodes tab, all workflow nodes that are scheduled to run are displayed. This is a ready only display.

Workflow Prerequisites

Before you perform any task with a workflow, the workflow prerequisites must be met.

The workflow prerequisites are:

- Create and establish a connection to the database.

- Create a project under which the workflow is created.

Related Topics

- Creating a Connection

- Creating a Project

Workflow Script Requirements

Workflow script requirements include the following:

- Script File Character Set Requirements
Ensure that all script files are generated using UTF8 character set.

- Script Variable Definitions
Scripts have variable definitions that provide object names for the public objects created by the scripts.

- Scripts Generated
Scripts Generated is a kind of deployment where several general scripts such as master script, cleanup script and so on are generated.

- Running Scripts using SQL*Plus or SQL Worksheet
If you run generated scripts using either SQL*Plus or SQL Worksheet, and require input from users, then you must run a command before the generated SQL.

Script File Character Set Requirements

Ensure that all script files are generated using UTF8 character set.

Scripts can contain characters based on character sets that will not be handled well unless the script file is generated using UTF8 character set.

Script Variable Definitions

Scripts have variable definitions that provide object names for the public objects created by the scripts.

The Master Script is responsible for calling all underlying scripts in order. So, the variable definitions must be defined in the Master Script.

The variable Object Types enables you to change the name of the object names that are input to the scripts, such as tables or views, and models. By default, these names are the original table or view, and model names.

All generated scripts should be put under the same directory.

Scripts Generated

Scripts Generated is a kind of deployment where several general scripts such as master script, cleanup script and so on are generated.

The generated scripts are:

- Master Script: Starts all the required scripts in the appropriate order. The script performs the following:

- Validates if the version of the script is compatible with the version of the Data Miner repository that is installed.

- Creates a workflow master table that contains entries for all the underlying objects created by the workflow script.

- Contains generated documentation covering key usage information necessary to understand operation of scripts.

- Cleanup Script: Drops all objects created by the workflow script. The Cleanup Script drops the following objects:

- Hidden objects, such as the table generated for Explore Data.

- Public objects, such as Model Names created by Build Nodes.

- Tables created by a Create Table Node.

- Workflow Diagram Image: Creates an image (.png file) of the workflow at the time of script generation. The entire workflow is displayed.

The other scripts that are generated depend on the nodes in the chain. Table 4-1 lists the nodes and their corresponding script functionality.

Table 4-1 Nodes and Script Functionality

Node

Script Functionality

- Data Source node

- Transform node

- Aggregate node

- Join node

- Filter Rows node

- Sample node

- Model Details Node

- Apply node

- Apply Text node

- Filter Columns Details node

Creates a view reflecting the output of the node.

Filter Column node

Creates a view reflecting the output of the Filter Column node such as other Transform type nodes.

If the Attribute Importance setting is specified, then a table is generated containing the AI result (private).

Build Text node

For each text transformation, the following objects are created:

- Feature Table Name

- Oracle Text Policy Object

Creates a view reflecting the output of the Build Text node. This is essentially the same output as an Apply Text node

Classification Build node

A model is created for each model build specification.

A master test result table is generated to store the list of test result tables generated per model.

GLM Model Row Diagnostics Table is created if row diagnostics is turned on.

Each Model Test has one table generated for each of the following test results: Performance, Performance Matrix, ROC (for binary classification only).

Each Model Test has one table for each of the following tests per target value (up to 100 maximum target values): List and Profit.

Regression Build node

A model is created for each model build specification.

GLM Model Row Diagnostics Table is created if row diagnostics is turned on.

A master test result table is generated to store the list of test result tables generated per model.

Each Model Test will have one table generated for each of the following test results: Performance and Residual.

- Clustering Build

- Anomaly Detection Build

- Feature Extraction Build

- Association Build

A model is created for each model build specification.

Test Node (Classification)

A master test result table is generated to store the list of test result tables generated per model.

GLM Model Row Diagnostics Table is created if row diagnostics is turned on.

Each Model Test has one table generated for each of these test results: Performance, Performance Matrix, ROC (for binary classification only).

Each Model Test has one table for each of Lift and Profit per target value up to 100 maximum target values.

Test Node (Regression)

GLM Model Row Diagnostics Table is created if row diagnostics is turned on.

A master test result table is generated to store the list of test result tables generated per model.

Each Model Test has one table generated for each of Performance and Residual.

- Model Node

- Text Reference Node

No scripts are generated. These nodes are just reference nodes to metadata.

Running Scripts using SQL*Plus or SQL Worksheet

If you run generated scripts using either SQL*Plus or SQL Worksheet, and require input from users, then you must run a command before the generated SQL.

Run the following command before the generated SQL:

set define off

You can either run this new line separately before running the generated SQL. You can also run the new line along with the generated SQL.

About Nodes

Nodes define the data mining operations in a workflow.

A workflow consists of one or more nodes that are connected by a link. The node categories, listed in Table 4-2, are available in the Components pane.

- Node Name and Node Comments
Every node must have a name and may have a comment. Name assignment is fully validated to assure uniqueness. Oracle Data Miner generates a default node name.

- Node Types
Oracle Data Miner provides different types of nodes for specific purposes such as build, model, linking, data mining operations, data transformation and so on.

- Node States
A node is always associated with a state which indicates its status.

Node Name and Node Comments

Every node must have a name and may have a comment. Name assignment is fully validated to assure uniqueness. Oracle Data Miner generates a default node name.

When a new node of particular type is created, its default name is based on the node type, such as Class Build for a Classification node. If a node with that name already exists, then the name is appended with 1 to make it unique. So if Class Build exists, then the node is named Class Build 1. If Class Build 1 exists, then 2 is appended so that the third Classification node is named Class Build 2, and so on. Each node type is handled independently. For example, Filter Columns node have its own sequence, different from the sequence of Classification nodes.

You can change the default name to any name that satisfies the following requirements:

- Node names must be unique within the workflow.

- Node names must not contain any / (slash) characters.

- Node names must be at least one character long. Maximum length of node name is 128 characters.

To change a node name, either change it in the Details tab of Properties or select the name in the workflow and type the new name.

Comments for the node are optional. If you provide any comments, then it must be not more than 4000 characters long.

Node Types

Oracle Data Miner provides different types of nodes for specific purposes such as build, model, linking, data mining operations, data transformation and so on.

Table 4-2 lists the different categories of nodes:

Table 4-2 Types of Nodes

Type

Description

Model Nodes

They specify models to build or models to add to a workflow.

Model Operations

They evaluate and apply models.

Data Nodes

They specify data for mining operation, data transformation or to save data to a table.

Transforms Nodes

They perform one or more transformation on the table or tables identified in a Data node.

Predictive Query Nodes

They create predictive results without the need to build models. The predictive queries automatically generate refined predictions based on data partitions.

Text Nodes

They prepare data sources that contain one or more text columns so that the data can be used in Build and Apply models.

Link Nodes

They provide a way to link or connect nodes.

Node States

A node is always associated with a state which indicates its status.

Table 4-3 lists the states of a node.

Table 4-3 Node States

Node States

Description

Graphical Indicator

Invalid

Indicates that the node has not been completely defined and is not ready for execution.

Most nodes must be connected to be valid. That is, they must have the input defined. A Data node does not need to be connected to be valid.

Error

Indicates that the node attempted to run but encountered an error. For nodes that perform several tasks such as build several models, any single failure sets the status of the node to Error.

You must correct all problems to clear the Error state.

Any change clears the Error state to Ready, if the problem is a server runtime failure not attributable to standard specification validations. To find out if the problem is really fixed, run the node.

Ready

Indicates that the node is properly defined and is ready for execution.

Nodes in Ready state are also known as Valid.

No graphical indicator

Complete

Indicates that the node execution is successfully completed.

Warning

Indicates that the node execution is complete but not with the expected result.

Working with Nodes

You can perform the following tasks with any nodes.

- Add Nodes or Create Nodes
You create or add nodes to the workflow.

- Copy Nodes
You can copy one or more nodes, and paste them into the same workflow or into a different workflow.

- Edit Nodes
You can edit nodes by using any one of the following ways:

- Link Nodes
Each link connects a source node to a target node.

- Refresh Nodes
Nodes such as Data Source node, Update Table node, and Model node rely on database resources for their definition. It may be necessary to refresh a node definition if the database resources change.

- Run Nodes
You perform the tasks specified in the workflow by running one or more nodes.

- Performing Tasks from the Node Context Menu
The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

Add Nodes or Create Nodes

You create or add nodes to the workflow.

For the node to be ready to run, you may have to specify information such as a table or view for a Data Source node or the target for a Classification node. To specify information, you edit nodes. You must connect nodes for the workflow to run. For example, you specify input for a Build node by connecting a Data node to a Build node.

To add nodes to a workflow:

1. Load the workflow.

2. In the Components pane, go to the Workflow Editor and expand the section for node. You can create and connect the following types of nodes in the workflow:

- Model Nodes

- Data Nodes

- Transforms Nodes

- Model Operations

- Predictive Query Nodes

- Text Nodes

- Link Nodes

Copy Nodes

You can copy one or more nodes, and paste them into the same workflow or into a different workflow.

Copy and paste does not carry with it any mining model or results that belong to the original nodes. Since model names must be unique, unique names are assigned to models, using original name as a starting point.

For example, when a copied Build node is pasted into a workflow, the paste operation creates a new Build node with settings identical to those of the original node. However, models or test results do not exist until the node runs.

Related Topics

- Copy

Edit Nodes

You can edit nodes by using any one of the following ways:

- Editing Nodes through Edit Dialog Box
The Edit dialog box for each node gives you the option to provide and edit settings related to the node.

- Editing Nodes through Properties
The Properties pane of a node is the pane that opens when a node in a workflow is selected. You can dock this pane.

Editing Nodes through Edit Dialog Box

The Edit dialog box for each node gives you the option to provide and edit settings related to the node.

To display the Edit dialog box of any node:

1. Double-click the node or right-click the node and select Edit.

2. The Edit dialog box opens. Make the edits to the node as applicable, and click OK.

For some nodes, such as Data Source node, the Edit Data Source Node dialog box automatically opens either when the node is dropped in to the workflow or when an input node is connected to a node.

Editing Nodes through Properties

The Properties pane of a node is the pane that opens when a node in a workflow is selected. You can dock this pane.

To edit a node through Properties pane:

1. Click the node that you want to edit. The Properties pane for the node is displayed in the lower right pane.

2. Click the icon to edit the node. For all other edits, click the respective sections in the Properties pane.

All nodes have one common section named Details in the Properties pane. This section contains the node name and comment. The other sections in the Properties pane depend on the type of node.

3. The corresponding Edit dialog box opens. Make the edits to the node as applicable, and click OK.

Link Nodes

Each link connects a source node to a target node.

A workflow is a directional graph. That is, it consists of nodes that are connected in order. To create a workflow, you connect or link nodes. When you connect two nodes, you indicate a relationship between the nodes. For example, to specify the data for a model build, link a Data Source node to a Model Build node.

You can link nodes, delete links, and cancel links using the following options:

- Linking Nodes in Components Pane
You can connect two or more nodes by using the Linking nodes option.

- Node Connection Dialog Box
In the Node Connection dialog box, you can link two or more nodes.

- Change Node Position
You can change the position of a node in a workflow.

- Connect Option in Diagram Menu
Use the Connect option in the diagram menu to link two or more nodes.

- Deleting a Link
You can delete an existing link, by selecting the link and pressing the DELETE key.

- Cancelling a Link
You can cancel a link while you are linking it, by pressing the ESC key.

Linking Nodes in Components Pane

You can connect two or more nodes by using the Linking nodes option.

To connect two nodes using the Linking Nodes option:

1. In the Components pane, expand the Linking Nodes section.

2. Click Link. Move the cursor to the source node and click. From the source node, drag the link that appears to the target node, and click again.

- If the link is valid, then clicking the target node creates the link.

- If the link is invalid, then the line does not terminate. To end the link process without completing a link, either press ESC or click in the Components pane.

Node Connection Dialog Box

In the Node Connection dialog box, you can link two or more nodes.

To link two nodes :

1. In the Components pane, expand the Linking Nodes section.

2. Click the Link icon and press ENTER. This opens the Select Source And Destination For A New Link dialog box.

3. Select the source node in the Source List and the target node in the Destination List.

The Source List lists all the nodes in the workflow. The Destination List lists the allowed target nodes for the selected node.

4. Click OK.

Change Node Position

You can change the position of a node in a workflow.

You can change the position of workflow nodes in these ways:

- Drag: To drag a node, click the node. Without releasing the mouse button, drag the node to the desired location. Links to the node are automatically repositioned.

- Adjust with arrow keys: To adjust the position by small increments, select the node. Then press and hold Shift + CONTROL keys. Use the arrow keys to move the node.

Connect Option in Diagram Menu

Use the Connect option in the diagram menu to link two or more nodes.

To use the Connect option:

1. Select the source node in the workflow.

2. In Data Miner menu bar, click Diagram and select Connect.

3. Move the cursor from the source node to the target node and click again.

- If the link is valid, then clicking the target node creates the link.

- If the link is invalid, then the line does not terminate. To end the link process without completing a link, press ESC.

Deleting a Link

You can delete an existing link, by selecting the link and pressing the DELETE key.

You may want to delete a link between two or more nodes in a workflow, if required.

Cancelling a Link

You can cancel a link while you are linking it, by pressing the ESC key.

To cancel a link while you are linking it, press the ESC key or select another item in the Components pane.

Refresh Nodes

Nodes such as Data Source node, Update Table node, and Model node rely on database resources for their definition. It may be necessary to refresh a node definition if the database resources change.

To refresh a node:

1. Click the node that you want to refresh.

2. In the right pane, go to Properties, and click the applicable section:

- Data: For Data Source node

- Model: For a Model node

- Columns: For an Update Table node

3. Click the icon. Data Miner connects to the database and validates the attributes or models. The status of the attribute or model is set to either:

- Valid: Indicated by the node without any mark on its top right corner.

- Invalid: Indicated by the mark or mark on the top right corner of the node.

Other possible validation error scenarios include:

- Source table not present: Error message states that the source table or attribute is missing. You have the option to:

- Select another table or replace a missing attribute.

- Leave the node in its prior state by clicking Cancel.

- Model is invalid: Click the Model refresh button on the Properties model tool bar to make the node valid. For Model nodes, the node becomes Invalid if a model is found to be invalid. The Model node cannot be run until the node is made Valid.

- Model is missing: If the Model node is run and the server determines that the model is missing, then the node is set to Error. You can rerun the model after the missing model is replaced.

Run Nodes

You perform the tasks specified in the workflow by running one or more nodes.

If a node depends on outputs of one or more parent nodes, then the parent node runs automatically only if the outputs required by the running node are missing. You can also run one or more nodes by selecting the nodes and then clicking in the toolbar of the workflow.

Note:

Nodes cannot be run always. If any ancestor node is in the Invalid state and its outputs are missing, then the child nodes that depend on it cannot run.

Each node in a workflow has a state that indicates its status.

You can run a node by clicking the following options in the context menu:

- Run

- Force Run

Related Topics

- Node States

- Run

- Force Run

Performing Tasks from the Node Context Menu

The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

Right-click a node to display the context list for the node. The context menu includes the following:

- Connect
Use the Connect option to link nodes in a workflow.

- Run
Use the Run option to execute the tasks specified in the nodes that comprise the workflow.

- Force Run
Use the Force Run option to rerun one or more nodes that are complete.

- Create Schedule
Use the Create Schedule option to define a schedule to run a workflow at a definite time and date.

- Edit
Use the Edit option to edit the default settings of a node.

- View Data
Use the View Data option to view the data contained in a Data node.

- View Models
Use the View Models option to view the details of the models that are built after running the workflow.

- Generate Apply Chain
Use the Generate Apply Chain to create a new node that contains the specification of a node that performs a transformation.

- Refresh Input Data Definition
Use the Refresh Input Data Definition option if you want to update the workflow with new columns, that are either added or removed.

- Show Event Log
Use the Show Event Log option to view information about events in the current connection, errors, warnings, and information messages.

- Deploy
Use the Deploy option to deploy a node or workflow by creating SQL scripts that perform the tasks specified in the workflow.

- Show Graph
The Show Graph option opens the Graph Node Editor.

- Cut
Use the Cut option to remove the selected object, which could be a node or connection.

- Copy
Use the Copy option to copy one or more nodes and paste them into the same workflow or a different workflow.

- Paste
Use the Paste option to paste the copied object in the workflow.

- Extended Paste
Use the Extended Paste option to preserve node and model names while pasting them.

- Select All
Use the Select All option to select all the nodes in a workflow.

- Performance Settings
Use the Performance Settings option to edit Parallel settings and In-Memory settings of the nodes.

- Toolbar Actions
Use the Toolbar Action option to select actions in the toolbar from the context menu.

- Show Runtime Errors
Use the Show Runtime Errors to view errors related to node failure during runtime. This option is displayed only when running of the node fails at runtime.

- Show Validation Errors
Use the Show Validation Errors option to view validation errors, if any.

- Save SQL
Use the Save SQL option to generate SQL script for the selected node.

- Validate Parents
Use the Validate Parents option to validate all parent nodes of the current node.

- Compare Test Results
Use the Compare Test Results option to view and compare test results of models that are built successfully.

- View Test Results
Use the View Test Results option to view the test results of the selected model. This option is applicable only for Classification and Regression models.

- Go to Properties
Use the Go to Properties option to open the Properties pane of the selected node.

- Navigate
Use the Navigate option to view the links available from the selected node.

Connect

Use the Connect option to link nodes in a workflow.

To connect nodes:

1. Right-click a node and click Connect. Alternately, go to Diagram and click Connect.

2. Use the cursor to draw a line from this node to the target node.

3. Click to establish the connection. Note the following:

- You can create only valid connections.

- You can create only one connection between any two nodes.

- You can remove a connection by pressing the ESC key.

Related Topics

- Connect

Run

Use the Run option to execute the tasks specified in the nodes that comprise the workflow.

The Data Miner server runs workflows asynchronously. The client does not have to be connected. You can run one or more nodes in the workflow:

- To run one node: Right-click the node and select Run.

- To run multiple nodes simultaneously: Select the nodes by holding down the Ctrl key and click each individual node. Then right-click any selected node and select Run.

If a node depends on outputs of one or more parent nodes, then the parent node runs automatically only if the outputs required by the running node are missing.

Force Run

Use the Force Run option to rerun one or more nodes that are complete.

Force Run deletes any existing models before building them once again.

To select more than one node, click the nodes while holding down the Ctrl key.

You can Force Run a node at any location in a workflow. Depending on the location of the node in the workflow, you have the following choices for running the node using Force Run:

- Selected Node

- Selected Node and Children (available if the node has child nodes)

- Child Node Only (available if the node one or more child nodes)

- Selected Node and Parents (available if the node has parent nodes)

Create Schedule

Use the Create Schedule option to define a schedule to run a workflow at a definite time and date.

In the Create Schedule dialog box, you can create schedules for your workflows. To create workflow schedule:

1. Start Date: Select a date to set as the start date of the schedule. Click to select a date.

2. Repeat: Select any one of the following options:

- None: To schedule the workflow to run only once at the defined time.

- Every Day: To schedule the workflow to run daily at the specified time.

- Every Week: To schedule the workflow to run weekly at the specified time.

- Custom: To customize your workflow schedule, click Custom. This opens the Repeat dialog box, where you can set how frequently the workflow should run.

3. End Repeat: You can select any one of the following options:

- None: To continue running the workflow every hour.

- After: Select a number by clicking the arrows. This runs the workflow every hour, and would stop after the number of hours you have selected here. For example, if you select 8, then the workflow will run every hour, and after 8 hours, it will stop.

- On Date: Select a particular date by clicking the calendar icon.

4. Select Use Existing Schedule, and select a schedule from the drop-down list if you want to schedule the workflow as per the selected schedule.

- Click to edit the selected schedule in the Schedule dialog box.

- Click to add a new schedule. You can also edit the selected schedule, and add it here.

- Click to delete the selected schedule.

5. Click OK.

To save the workflow schedule settings, click . You can provide a name for the schedule in the Save a Schedule dialog box.

Related Topics

- Repeat

- Save a Schedule

Edit

Use the Edit option to edit the default settings of a node.

Nodes have default algorithms and settings. When you edit a node, the default algorithms and settings are modified. You can edit a node in any one of the following ways:

- Edit nodes using the Edit dialog box

- Edit nodes through Properties UI

Related Topics

- Editing Nodes through Edit Dialog Box

- Editing Nodes through Properties

View Data

Use the View Data option to view the data contained in a Data node.

The Data nodes are Create Table or View node, Data Source node, Explore Data node, Graph node, SQL Query node, and Update Table node.

Related Topics

- Data Source Node Viewer

View Models

Use the View Models option to view the details of the models that are built after running the workflow.

To view models, you must select a model from the list to open the model viewer. A model must be built successfully before it can be viewed.

Generate Apply Chain

Use the Generate Apply Chain to create a new node that contains the specification of a node that performs a transformation.

If you have several transformations performed in sequence, for example, Sample followed by a Custom transform, then you must select Generate Apply Chain for each transformation in the sequence.You must connect the individual nodes and connect them to an appropriate data source.

Generate Apply Chain helps you create a sequence of transformations that you can use to ensure that new data is prepared in the same way as existing data. For example, to ensure that Apply data is prepared in the same way as Build data, use this option.

The Generate Apply Chain option is not valid for all nodes. For example, it does not copy the specification of a Build node.

Refresh Input Data Definition

Use the Refresh Input Data Definition option if you want to update the workflow with new columns, that are either added or removed.

The Refresh Input Data Definition option is equivalent to SELECT* capability in the input source. The option allows you to quickly refresh your workflow definitions to include or exclude columns, as applicable.

Note:
The Refresh Input Data Definition option is available as a context menu option in Data Source nodes and SQL Query nodes.

Show Event Log

Use the Show Event Log option to view information about events in the current connection, errors, warnings, and information messages.

Clicking the Show Event Log option opens the View and Event Log dialog box.

Related Topics

- View an Event Log

Deploy

Use the Deploy option to deploy a node or workflow by creating SQL scripts that perform the tasks specified in the workflow.

The scripts generated by Deploy are saved to a directory.

Note:

You must run a node before deploying it.

You can generate a script that replicates the behavior of the entire workflow. Such a script can serve as the basis for application integration or as a light-weight deployment than the alternative of installing the Data Miner repository and workflows in the target and production system.

To deploy a workflow or part of a workflow:

1. Right-click a node and select Deploy.

2. Select any one of the deployment options:

- Selected node and dependent nodes

- Selected node, dependent nodes, and child nodes

- Selected node and connected nodes

3. After selecting the deployment option, the Generate SQL Script wizard opens. In the wizard, enter details for the following:

Related Topics

- Deploy Workflows using Object Generation Scripts

- Running Workflow Scripts

Show Graph

The Show Graph option opens the Graph Node Editor.

All graphs are displayed in the Graph Node Editor.

Related Topics

- Graph Node Editor

Cut

Use the Cut option to remove the selected object, which could be a node or connection.

You can also delete objects by selecting them and pressing DELETE on your keyboard.

Copy

Use the Copy option to copy one or more nodes and paste them into the same workflow or a different workflow.

To copy and paste nodes:

1. Select the nodes to copy. To select several nodes, hold down the Ctrl key when you click the nodes.

The selected node is highlighted. In this example Classification is selected. The other node is not selected.

2. Right-click and select Copy from the context menu. Alternately, you can press Ctrl+C to copy the selected nodes.

Note:

Copying and pasting nodes do not carry any mining models or results from the original node.

Paste

Use the Paste option to paste the copied object in the workflow.

To paste an object, right-click the workflow and click Paste. Alternately, you can press Ctrl+V.

Note:

Node names and model names are changed to avoid naming collisions. To preserve names, use the option Extended Paste.

Related Topics

- Copy

- Extended Paste

Extended Paste

Use the Extended Paste option to preserve node and model names while pasting them.

The default behavior of Paste is to change node names and model names to avoid naming collisions.

To go to the Extended Paste option, right-click the workflow and click Extended Paste. Alternately, you can press Control+Shift+V.

Note:

If model names are not unique, then the models may be overwritten when they are rebuilt.

Related Topics

- Copy

Select All

Use the Select All option to select all the nodes in a workflow.

The selected nodes and links are highlighted in a dark blue border.

Performance Settings

Use the Performance Settings option to edit Parallel settings and In-Memory settings of the nodes.

If you click Performance Settings in the context menu, or if you click Performance Options in the workflow toolbar, then the Edit Selected Node Settings dialog box opens. It lists all the nodes that comprise the workflow. To edit the settings in the Edit Selected Node Settings dialog box:

- Click Parallel Settings and select:

- Enable: To enable parallel settings in the selected nodes in the workflow.

- Disable: To disable parallel settings in the selected nodes in the workflow.

- All: To turn on parallel processing for all nodes in the workflow.

- None: To turn off parallel processing for all nodes in the workflow.

- Click In-Memory Settings and select:

- Enable: To enable In-Memory settings for the selected nodes in the workflow.

- Disable: To disable In-Memory settings for the selected nodes in the workflow.

- All: To turn on In-Memory settings for the selected nodes in the workflow.

- None: To turn off In-Memory settings for all nodes in the workflow

- Click to set the Degree of Parallel, and In-Memory settings such as Compression Method, and Priority Levels in the Edit Node Performance Settings dialog box.
If you specify parallel settings for at least one node, then this indication appears in the workflow title bar:

Performance Settings is either On for Selected nodes, On (for All nodes), or Off. You can click Performance Options to open the Edit Selected Node Settings dialog box.

- Click to edit default the preferences for parallel processing.

- Edit Node Default Settings: You can edit the Parallel Settings and In-Memory settings for the selected node in the Performance Options dialog box. You can access the Performance Options dialog box from the Preferences options in the SQL Developer Tools menu.

- Change Settings to Default

Related Topics

- About Parallel Processing

- Edit Node Performance Settings

- Performance Options

Toolbar Actions

Use the Toolbar Action option to select actions in the toolbar from the context menu.

Current actions are Zoom In and Zoom Out.

Related Topics

- Managing Workflows using Workflow Controls

Show Runtime Errors

Use the Show Runtime Errors to view errors related to node failure during runtime. This option is displayed only when running of the node fails at runtime.

The Event Log opens with a list of errors. Select the error to see the exact message and details.

Related Topics

- View an Event Log

Show Validation Errors

Use the Show Validation Errors option to view validation errors, if any.

This option is displayed only when there are validation errors. For example, if an Association node is not connected to a Data Source node, then select Show Validation Errors to view the validation error No build data input node connected.

You can also view validation errors by moving the mouse over the node. The errors are displayed in a tool tip.

Save SQL

Use the Save SQL option to generate SQL script for the selected node.

To generate SQL script for the selected node:

1. Right-click the node and click Save SQL.

2. Select any one of the options to save the generated SQL script:

- SQL to Clipboard

- SQL to File

- SQL Script to Clipboard

- SQL Script to File

When you save to a file, the system provides a default location. You can browse to change this location. You can also create a folder for scripts.

The saved SQL includes SQL generated by the current node and all of its parent nodes that are data providers. The SQL lineage ends when it encounters a node that represents persisted objects, such as tables or models.

The generated script does not generate all behavior of the node. The script does not create any objects. For example, if you select Save SQLfor a Create Table node, then it does not generate a script to create the table. Instead, it generates a script to query the created table.

Related Topics

- Deploy Workflows using Data Query Scripts

Validate Parents

Use the Validate Parents option to validate all parent nodes of the current node.

To validate parent nodes of a node, right-click the node and select Validate Parents.

You can validate parent nodes when the node is in Ready, Complete and Error state. All parent nodes must be in completed state.

Compare Test Results

Use the Compare Test Results option to view and compare test results of models that are built successfully.

For Classification and Regression models, this option displays the test results for all successfully built models to allow you to pick the model that best solves the problem.

View Test Results

Use the View Test Results option to view the test results of the selected model. This option is applicable only for Classification and Regression models.

The test results are displayed in the test viewer of the respective models:

Related Topics

- Classification Model Test Viewer

- Regression Model Test Viewer

Go to Properties

Use the Go to Properties option to open the Properties pane of the selected node.

Related Topics

- Managing Workflows and Nodes in the Properties Pane

Navigate

Use the Navigate option to view the links available from the selected node.

Note:
The Navigate option is enabled only if there are links to other nodes.

Navigate displays the collection of links available from this node. Selecting one of the links selects the link and the selected link is highlighted in the workflow. The link itself has context menu options as well so you can right click and continue with the Navigate option. You can also use the arrow keys to progress to the next node.

About Parallel Processing

In Parallel Query or Parallel Processing, multiple processes work simultaneously to run a single SQL statement.

Oracle Data Miner uses the specifications in a workflow to create SQL queries. These queries are passed and run in the Oracle Database.

By dividing the work among multiple processes, the Oracle Database can run the statement more quickly. For example, suppose four processes handle four different quarters in a year instead of one process handling all four quarters by itself.

The benefits of Parallel Processing:

- Reduces response time for data-intensive operations on large databases such as data warehouses.

- Enhances performance of symmetric multiprocessing (SMP) as statement processing are split up among multiple systems. Certain types of OLTP and hybrid systems also benefit from parallel processing.

In Oracle RAC systems, the service placement of a specific service controls parallel processing. Specifically, parallel processes run on the nodes on which the service is configured. By default, Oracle Database runs parallel processes only on an instance that offers the service used to connect to the database. This does not affect other parallel operations such as parallel recovery or the processing of GV$ queries.

Parallel processing must be configured by a Database Administrator (DBA). For more information on parallel processing in Oracle Database, see:

- Oracle Database Data Warehousing Guide or Oracle Database VLDB and Partitioning Guide for more information about parallel processing

- Oracle Real Application Clusters Administration and Deployment Guide for considerations about parallel processing in Oracle RAC environments

- Parallel Processing Use Cases
This section lists some common use cases where you can use parallel processing.

- Oracle Data Mining Support for Parallel Processing
Model scoring is done in parallel for all algorithms and data in Oracle Database 12.1 and later.

Related Topics

- Oracle Database Data Warehousing Guide

- Oracle Database VLDB and Partitioning Guide

- Oracle Real Application Clusters Administration and Deployment Guide

Parallel Processing Use Cases

This section lists some common use cases where you can use parallel processing.

- Premise of the Parallel Processing Use Case
The use cases for parallel processing lists the conditions under which models are built in parallel.

- Making Transformation Run Faster, using Parallel Processing and Other Methods
You can make transformations run faster by using Parallel Processing and other techniques.

- Running Graph Node in Parallel
You can run Graph nodes in parallel. Even if no other nodes are parallel, performance may improve.

- Running a Node in Parallel to Test Performance
You can run parallel processing on a node just once to see if parallel processing results in improved performance.

Premise of the Parallel Processing Use Case

The use cases for parallel processing lists the conditions under which models are built in parallel.

Premise of the use case:

- If the input source for a model is a table defined with parallel and no intervening workflow nodes generate a table that changes this state, then models are built in parallel without any changes to the workflow settings.

- If the input source is not already defined in parallel, you can still build the model in parallel:

- For Classification and Regression models: Turn on Parallel Processing for the workflow. The setting for Classification and Regression will be to Split input into Tables with the Parallel option.

- For all models: Turn on Parallel Processing for the workflow. Insert a Create Table node before the Build node. Use the created table as input to the models.

Making Transformation Run Faster, using Parallel Processing and Other Methods

You can make transformations run faster by using Parallel Processing and other techniques.

The techniques are:

- Turning on Parallel Processing for the workflow: All nodes that have a form of sample input could have some benefit. If the sample size is small, then the Oracle Database may not generate parallel queries. But a complex query could still trigger parallel processing.

- Adding a Create Table node: You can add a Create Table node after expensive transformations to reduce repetitive querying costs.

- Adding an index to Create Table node: You can add an index to a Create Table node to improve downstream join performance.

Running Graph Node in Parallel

You can run Graph nodes in parallel. Even if no other nodes are parallel, performance may improve.

To run a Graph node in parallel:

1. Set parallel processing for the entire workflow.

2. Turn off parallel processing for all nodes except for the Graph nodes.

3. Run the Graph nodes. Graph node sample data is now generated in parallel. If the Graph node sample is small or the query is simple, then the query may not be made parallel.

Related Topics

- Setting Parallel Processing for a Node or Workflow

Running a Node in Parallel to Test Performance

You can run parallel processing on a node just once to see if parallel processing results in improved performance.

To run parallel processing:

1. Set parallel processing for the entire workflow.

2. Run the workflow. Note the performance.

3. Now, turn off parallel processing for the workflow.

Related Topics

- Setting Parallel Processing for a Node or Workflow

Oracle Data Mining Support for Parallel Processing

Model scoring is done in parallel for all algorithms and data in Oracle Database 12.1 and later.

All algorithms do not support parallel build. For Oracle Database 12.1 and later, the following algorithms support parallel build:

- Decision Trees

- Naive Bayes

- Minimum Description Length

- Expectation Maximization

All other algorithms support serial build only.

Setting Parallel Processing for a Node or Workflow

By default, parallel processing is set to OFF for any node type.

Even if parallel processing is set to ON with a preference, the user can override the section for a specific workflow or node.

To set parallel processing for a node or workflow:

1. Right-click the node and select Performance Settings from the context menu. The Edit Selected Node Settings dialog box opens.

2. Click OK.

- Performance Settings
Use the Performance Settings option to edit Parallel settings and In-Memory settings of the nodes.

- Edit Node Performance Settings
The Edit Node Performance Settings window opens when you click edit icon in the Edit Selected Node Settings. You can set Parallel Processing settings and In-Memory settings for one or all nodes in the workflow.

- Edit Node Parallel Settings
In the Edit Node Parallel Settings dialog box, you can provide parallel query settings for the selected node.

Performance Settings

Use the Performance Settings option to edit Parallel settings and In-Memory settings of the nodes.

If you click Performance Settings in the context menu, or if you click Performance Options in the workflow toolbar, then the Edit Selected Node Settings dialog box opens. It lists all the nodes that comprise the workflow. To edit the settings in the Edit Selected Node Settings dialog box:

- Click Parallel Settings and select:

- Enable: To enable parallel settings in the selected nodes in the workflow.

- Disable: To disable parallel settings in the selected nodes in the workflow.

- All: To turn on parallel processing for all nodes in the workflow.

- None: To turn off parallel processing for all nodes in the workflow.

- Click In-Memory Settings and select:

- Enable: To enable In-Memory settings for the selected nodes in the workflow.

- Disable: To disable In-Memory settings for the selected nodes in the workflow.

- All: To turn on In-Memory settings for the selected nodes in the workflow.

- None: To turn off In-Memory settings for all nodes in the workflow

- Click to set the Degree of Parallel, and In-Memory settings such as Compression Method, and Priority Levels in the Edit Node Performance Settings dialog box.
If you specify parallel settings for at least one node, then this indication appears in the workflow title bar:

Performance Settings is either On for Selected nodes, On (for All nodes), or Off. You can click Performance Options to open the Edit Selected Node Settings dialog box.

- Click to edit default the preferences for parallel processing.

- Edit Node Default Settings: You can edit the Parallel Settings and In-Memory settings for the selected node in the Performance Options dialog box. You can access the Performance Options dialog box from the Preferences options in the SQL Developer Tools menu.

- Change Settings to Default

Related Topics

- About Parallel Processing

- Edit Node Performance Settings

- Performance Options

Edit Node Performance Settings

The Edit Node Performance Settings window opens when you click edit icon in the Edit Selected Node Settings. You can set Parallel Processing settings and In-Memory settings for one or all nodes in the workflow.

To set Parallel Query settings and In Memory settings:

- Select Parallel Query On to set parallel processing for the node. If you specify parallel processing for a node type, then the query generated by the node may not run in parallel.

- System Determined: This is the default degree of parallelism.

- Degree Value: To specify a value for degree of parallelism, select this option and choose a value by clicking the arrows. The default value is 1. The specified value is displayed in the Degree of Parallel column for the node type in the Performance Option and Edit Selected Node Settings dialog boxes.

- Select In-Memory Columnar option to set the compression method and priority level for the selected node. The selected settings are displayed in the In-Memory Settings option in the Performance Option dialog box.

Note:
The In Memory option is available in Oracle Database 12.1.0.2 and later.

- Compression Method: Allows you to set a compression method for the data.

- None: In this method, the data is not compressed.

- Low: This method results in the best query performance.

- Medium: This method optimizes the data for DML operations and compresses the IM column

- High: This method results in excellent query performance.

- Higher: This method results in good query performance.

- Highest: This method results in a fair query performance.

- Priority Level: The priority level that you set here determines when the data of the database object is populated in the IM column store.

- None: This is the default setting when priority is not included in the INMEMORY clause.

- Low: Displays the data before database objects with the priority level NONE and after priority levels: MEDIUM, HIGH or HIGHEST.

- Medium: Displays the data before database objects with the priority levels: NONE or LOW, and after priority levels: HIGH or HIGHEST.

- High: Displays the data before database objects with the priority levels: NONE, LOW, or MEDIUM and after priority level: HIGHEST.

- Highest: Displays the data before database objects with the priority levels: NONE, LOW, MEDIUM, or HIGH.

Related Topics

- About Parallel Processing

- Performance Settings

- Performance Options

Edit Node Parallel Settings

In the Edit Node Parallel Settings dialog box, you can provide parallel query settings for the selected node.

To set parallel query settings for the selected node.

1. Click Parallel Query On.

2. For Degrees of Parallel, select:

- System Determined: This is the default degree of parallelism.

- Degree Value: To specify a value for degree of parallelism, select this option and choose a value by clicking the arrows. The default value is 1. The specified value is displayed in the Degree of Parallel column for the node type in the Performance Options and Edit Selected Node Settings dialog boxes.

3. Click OK.

Related Topics

- Performance Options

- Performance Settings

About Oracle Database In-Memory

The In-Memory Column store (IM column store) is an optional, static System Global Area (SGA) pool that stores copies of tables and partitions in a special columnar format in Oracle Database 12c Release 1 (12.1.0.2) and later.

The IM column store does not replace the buffer cache. It acts as a supplement so that both memory areas can store the same data in different formats.

- Benefits of Oracle Database In-Memory Column Store
The IM column store enables the database to perform scans, joins, and aggregates much faster than when it uses the on-disk format exclusively.

- Use Cases of Oracle Database In-Memory
You can use Oracle Database In-Memory feature in areas where you have to handle vast amount of data and where you have the need for real time information based on complex analysis of this data.

Benefits of Oracle Database In-Memory Column Store

The IM column store enables the database to perform scans, joins, and aggregates much faster than when it uses the on-disk format exclusively.

The IM column store is particularly useful for:

- Scanning many rows and applying filters that use operators such as =, <, >, and IN

- Querying a subset of columns in a table. For example, selecting 5 of 100 columns

- Accelerating joins by converting predicates on small dimension tables into filters on a large fact table.

Business applications, ad-hoc analytic queries, and data warehouse workloads benefit most. Pure OLTP databases that perform short transactions using index lookups benefit less.

The IM column store also provides the following advantages:

- All existing database features are supported, including High Availability.

- No application changes are required. The optimizer automatically takes advantage of the columnar format.

- Configuration is simple.
The INMEMORY_SIZE initialization parameter specifies the amount of memory reserved for use by the IM column store. DDL statements specify the tablespaces, tables, partitions, or columns to be read in to the IM column store.

- Compression is optimized for query performance.
These compression techniques increase the effective memory bandwidth by enabling sessions to read more data into memory.

- Fewer indexes, materialized views, and OLAP cubes are required.
The reduction in the number of pre-built objects in reduced storage space and significantly less processing overhead.

Use Cases of Oracle Database In-Memory

You can use Oracle Database In-Memory feature in areas where you have to handle vast amount of data and where you have the need for real time information based on complex analysis of this data.

Some of the most common scenarios where Oracle Database In-Memory can be used are:

- Stock trading analysis: To analyze and execute trading data and generate information for brokers.

- Telecom routing: To route telecom connections based on real time data about connection status, load, errors, and response time of the nodes within the network. In this scenario, the goal is to provide an optimal telecom route within a second or less, after dialing a number.

- Fraud detection: To detect deviant patterns or anomalous transactions, by running the detection rules in In-Memory database tables. The complex fraud detection analysis can be done quickly and relevant notifications can be sent immediately after detecting a deviance.

5 Data Nodes

Data nodes specify data for a mining operation, to transform data, or to save data to a table. The input for Oracle Data Mining operations is a table or view in Oracle Database or an Oracle Data Miner node that is part of a data flow.

The Data nodes are available in the Data section in the Components pane. The following Data nodes enable you to specify data in a workflow and to create and modify tables:

- Create Table or View Node
The Create Table or View node is a type of node that enables you to save the results in a table in the schema to which you are connected.

- Data Source Node
A Data Source node defines source data for the workflow. For example, a Data Source node specifies the build data for a model.

- Explore Data Node
The Explore Data node provides the profile of any input data source. Explore Data Statistics can either be based on all data or on a sample of the data.

- Graph Node
A Graph node creates a two-dimensional graph of numeric data.

- SQL Query Node
SQL Query node writes SQL queries to perform special data preparation. Use the SQL Query node to provide input for a model build.

- Update Table Node
An Update Table node updates an existing table with selected columns from the data input to the node. Input columns are mapped to the existing table columns.

- Target Values Selection
The Target Values Selection dialog box displays the number of target values selected. The default setting is Automatic.

Create Table or View Node

The Create Table or View node is a type of node that enables you to save the results in a table in the schema to which you are connected.

For example, use a Create Table or View node to save the results of an Apply node to a table. The Create Table node automatically uses compression when possible. The Create Table node can run in parallel.

The benefits of Create Table or View node are:

- Data Persistence: A Create Table or View node saves the data that flow into the node as a view or table in the database. If you create a table, then the actual data persists. If you create a view, then the SQL definition (full lineage) persists. Output from this node is the data provided by the view or table.

- Performance Improvement: If you perform one or more complex transformations, such as Joins and Aggregations, and save the result of the transformations as a table, then the subsequent operations are faster. For example, you can perform an Aggregation and a Join, create a table that contains the results of the transformation, and then use the table as input for building the model. Therefore, you will create a table from the Join node. The Classification models are built against this table.

- Working with the Create Table or View Node
Lists the tasks you can perform using a Create Table or View node.

Related Topics

- About Parallel Processing

- Create Table Node and Compression

Working with the Create Table or View Node

Lists the tasks you can perform using a Create Table or View node.

- Creating a Create Table or View Node
You create a Create Table or View node to save a data flow to a table or view.

- Create Table Node and Compression
Oracle Data Miner creates tables in the Create Table node, and creates split data sets for testing in the Classification and Regression model build.

- Edit Create Table or View Node
You can modify the operation of the Create Table or View node in the Edit Create Table or View dialog box.

- Select Columns
By default, all columns are selected. You must select at least one attribute for the Create Table or View definition to be complete.

- View Create Table or View Data
You can view the data of a node in the Data Source Node viewer.

- Create Table or View Context Menu
The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

- Create the Table or View Node Properties
In the Properties pane, you can examine and change the characteristics or properties of a node.

Creating a Create Table or View Node

You create a Create Table or View node to save a data flow to a table or view.

You can connect a Create Table or View node to any node that create a data flow, such as an Apply node. To create a Create Table or View node:

1. In the Components pane, go to Workflow Editor. If the Components pane is not visible, then in the SQL Developer menu bar, go to View and click Components. Alternately, press Ctrl+Shift+P to dock the Components pane.

2. In the Data section, click the Create Table or View icon.

3. Drag and drop the node from the Components pane to the Workflow pane.
The node is added to the workflow. The GUI shows that the node has no data associated with it. Therefore, it cannot be run.

4. Right-click the node from which to create the table, and click Connect in the context menu.

5. Draw a line from the selected node to the Create Table or View node and click again.

6. You can accept the default settings of the Create Table or View node or edit the default settings. Click Edit in the context menu.

7. To create the table, right-click the Create Table or View node and select Run from the context menu.

The table is automatically compressed, if possible.

8. After running the node, right-click the node and select View Data to view the results.

Related Topics

- Edit Create Table or View Node

- Create Table Node and Compression

Create Table Node and Compression

Oracle Data Miner creates tables in the Create Table node, and creates split data sets for testing in the Classification and Regression model build.

When Oracle Data Miner creates a table, and if Oracle Data Miner determines that the data does not have nested columns DM_NESTED_*, then it uses compression to create the table.

Table compression does the following:

- Saves disk space

- Reduces memory use in the buffer cache

- Speeds up the running of queries during reads

For the Create Table node, Oracle Data Miner also verifies that a primary key is not defined and no indexes are defined.

Edit Create Table or View Node

You can modify the operation of the Create Table or View node in the Edit Create Table or View dialog box.

To edit a Create Table or View node:

1. Double-click the node or right-click and select Edit. The Edit Create Table or View dialog box opens.

2. In the Edit Create Table or View node dialog box, you can make the following changes:

- Name: Displays the default table name. You can change the default name of the table or view to any unique and valid name.

- Type: By default, the object type is Table. You can change the default to View by selecting the appropriate option.

- Storage Settings: Click to set the data compression method and logging settings when creating a table.

- Auto Input Column Selection: Deselect the check box to manually select and edit input columns. You can perform the following tasks:

- Delete columns: Select the column and click .

- Edit columns: Select the column and click . The Select Column dialog box opens.

Note:

If JSON data is present, then select the column and click to specify Data Guide settings in the Edit Data Guide dialog box.

- Edit the data type entry in the Target Type column for JSON data only. Click the data type in the Target column to select an option from the in-place drop-down list.

- Specify Key, Index and Alias for each column.

Note:

If you create a table that join another object, then adding an index will speed up the join.

- JSON Settings: Click JSON Settings to specify node settings that determines how Data Guides are generated. This option is applicable only for JSON data.

3. Click OK.

- Edit Storage Settings
In the Edit Storage Settings dialog box, you can set the logging settings and data compression method.

Related Topics

- JSON Settings

- Edit Data Guide

- Select Columns

Edit Storage Settings

In the Edit Storage Settings dialog box, you can set the logging settings and data compression method.

To edit logging settings and data comprehension method:

- Select Logging On to override any other settings specified as part of Parallel Processing settings.

- In the Compression Level section, set the data compression method by selecting any one of the following options:

- None: In this method, the data is not compressed.

- Low: This method results in the best query performance.

- Medium: This method optimizes the data for DML operations and compresses the IM column

- High: This method results in excellent query performance.

- Higher: This method results in good query performance.

- Highest: This method results in a fair query performance.

Select Columns

By default, all columns are selected. You must select at least one attribute for the Create Table or View definition to be complete.

You can perform the following tasks:

- Remove columns: Select the attribute in the Selected Attribute section and move it to the Available Attribute section. Click OK.

- Add columns: Select the attribute in theAvailable Attribute section and move it to the Selected Attribute section. Click OK.

View Create Table or View Data

You can view the data of a node in the Data Source Node viewer.

After the node runs successfully, right-click the node and select View Data. The data is displayed in the Data Source Node viewer.

Related Topics

- Data Source Node Viewer

Create Table or View Context Menu

The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

You can perform the following tasks:

- Connect
Use the Connect option to link nodes in a workflow.

- Run
Use the Run option to execute the tasks specified in the nodes that comprise the workflow.

- Force Run
Use the Force Run option to rerun one or more nodes that are complete.

- Create Schedule
Use the Create Schedule option to define a schedule to run a workflow at a definite time and date.

- Edit
Use the Edit option to edit the default settings of a node.

- View Data
Use the View Data option to view the data contained in a Data node.

- View Models
Use the View Models option to view the details of the models that are built after running the workflow.

- Generate Apply Chain
Use the Generate Apply Chain to create a new node that contains the specification of a node that performs a transformation.

- Refresh Input Data Definition
Use the Refresh Input Data Definition option if you want to update the workflow with new columns, that are either added or removed.

- Show Event Log
Use the Show Event Log option to view information about events in the current connection, errors, warnings, and information messages.

- Deploy
Use the Deploy option to deploy a node or workflow by creating SQL scripts that perform the tasks specified in the workflow.

- Show Graph
The Show Graph option opens the Graph Node Editor.

- Cut
Use the Cut option to remove the selected object, which could be a node or connection.

- Copy
Use the Copy option to copy one or more nodes and paste them into the same workflow or a different workflow.

- Paste
Use the Paste option to paste the copied object in the workflow.

- Extended Paste
Use the Extended Paste option to preserve node and model names while pasting them.

- Select All
Use the Select All option to select all the nodes in a workflow.

- Performance Settings
Use the Performance Settings option to edit Parallel settings and In-Memory settings of the nodes.

- Toolbar Actions
Use the Toolbar Action option to select actions in the toolbar from the context menu.

- Show Runtime Errors
Use the Show Runtime Errors to view errors related to node failure during runtime. This option is displayed only when running of the node fails at runtime.

- Show Validation Errors
Use the Show Validation Errors option to view validation errors, if any.

- Save SQL
Use the Save SQL option to generate SQL script for the selected node.

- Validate Parents
Use the Validate Parents option to validate all parent nodes of the current node.

- Compare Test Results
Use the Compare Test Results option to view and compare test results of models that are built successfully.

- View Test Results
Use the View Test Results option to view the test results of the selected model. This option is applicable only for Classification and Regression models.

- Go to Properties
Use the Go to Properties option to open the Properties pane of the selected node.

- Navigate
Use the Navigate option to view the links available from the selected node.

Related Topics

- Data Source Node Viewer

Connect

Use the Connect option to link nodes in a workflow.

To connect nodes:

1. Right-click a node and click Connect. Alternately, go to Diagram and click Connect.

2. Use the cursor to draw a line from this node to the target node.

3. Click to establish the connection. Note the following:

- You can create only valid connections.

- You can create only one connection between any two nodes.

- You can remove a connection by pressing the ESC key.

Related Topics

- Connect

Run

Use the Run option to execute the tasks specified in the nodes that comprise the workflow.

The Data Miner server runs workflows asynchronously. The client does not have to be connected. You can run one or more nodes in the workflow:

- To run one node: Right-click the node and select Run.

- To run multiple nodes simultaneously: Select the nodes by holding down the Ctrl key and click each individual node. Then right-click any selected node and select Run.

If a node depends on outputs of one or more parent nodes, then the parent node runs automatically only if the outputs required by the running node are missing.

Force Run

Use the Force Run option to rerun one or more nodes that are complete.

Force Run deletes any existing models before building them once again.

To select more than one node, click the nodes while holding down the Ctrl key.

You can Force Run a node at any location in a workflow. Depending on the location of the node in the workflow, you have the following choices for running the node using Force Run:

- Selected Node

- Selected Node and Children (available if the node has child nodes)

- Child Node Only (available if the node one or more child nodes)

- Selected Node and Parents (available if the node has parent nodes)

Create Schedule

Use the Create Schedule option to define a schedule to run a workflow at a definite time and date.

In the Create Schedule dialog box, you can create schedules for your workflows. To create workflow schedule:

1. Start Date: Select a date to set as the start date of the schedule. Click to select a date.

2. Repeat: Select any one of the following options:

- None: To schedule the workflow to run only once at the defined time.

- Every Day: To schedule the workflow to run daily at the specified time.

- Every Week: To schedule the workflow to run weekly at the specified time.

- Custom: To customize your workflow schedule, click Custom. This opens the Repeat dialog box, where you can set how frequently the workflow should run.

3. End Repeat: You can select any one of the following options:

- None: To continue running the workflow every hour.

- After: Select a number by clicking the arrows. This runs the workflow every hour, and would stop after the number of hours you have selected here. For example, if you select 8, then the workflow will run every hour, and after 8 hours, it will stop.

- On Date: Select a particular date by clicking the calendar icon.

4. Select Use Existing Schedule, and select a schedule from the drop-down list if you want to schedule the workflow as per the selected schedule.

- Click to edit the selected schedule in the Schedule dialog box.

- Click to add a new schedule. You can also edit the selected schedule, and add it here.

- Click to delete the selected schedule.

5. Click OK.

To save the workflow schedule settings, click . You can provide a name for the schedule in the Save a Schedule dialog box.

Related Topics

- Repeat

- Save a Schedule

Edit

Use the Edit option to edit the default settings of a node.

Nodes have default algorithms and settings. When you edit a node, the default algorithms and settings are modified. You can edit a node in any one of the following ways:

- Edit nodes using the Edit dialog box

- Edit nodes through Properties UI

Related Topics

- Editing Nodes through Edit Dialog Box

- Editing Nodes through Properties

View Data

Use the View Data option to view the data contained in a Data node.

The Data nodes are Create Table or View node, Data Source node, Explore Data node, Graph node, SQL Query node, and Update Table node.

Related Topics

- Data Source Node Viewer

View Models

Use the View Models option to view the details of the models that are built after running the workflow.

To view models, you must select a model from the list to open the model viewer. A model must be built successfully before it can be viewed.

Generate Apply Chain

Use the Generate Apply Chain to create a new node that contains the specification of a node that performs a transformation.

If you have several transformations performed in sequence, for example, Sample followed by a Custom transform, then you must select Generate Apply Chain for each transformation in the sequence.You must connect the individual nodes and connect them to an appropriate data source.

Generate Apply Chain helps you create a sequence of transformations that you can use to ensure that new data is prepared in the same way as existing data. For example, to ensure that Apply data is prepared in the same way as Build data, use this option.

The Generate Apply Chain option is not valid for all nodes. For example, it does not copy the specification of a Build node.

Refresh Input Data Definition

Use the Refresh Input Data Definition option if you want to update the workflow with new columns, that are either added or removed.

The Refresh Input Data Definition option is equivalent to SELECT* capability in the input source. The option allows you to quickly refresh your workflow definitions to include or exclude columns, as applicable.

Note:
The Refresh Input Data Definition option is available as a context menu option in Data Source nodes and SQL Query nodes.

Show Event Log

Use the Show Event Log option to view information about events in the current connection, errors, warnings, and information messages.

Clicking the Show Event Log option opens the View and Event Log dialog box.

Related Topics

- View an Event Log

Deploy

Use the Deploy option to deploy a node or workflow by creating SQL scripts that perform the tasks specified in the workflow.

The scripts generated by Deploy are saved to a directory.

Note:

You must run a node before deploying it.

You can generate a script that replicates the behavior of the entire workflow. Such a script can serve as the basis for application integration or as a light-weight deployment than the alternative of installing the Data Miner repository and workflows in the target and production system.

To deploy a workflow or part of a workflow:

1. Right-click a node and select Deploy.

2. Select any one of the deployment options:

- Selected node and dependent nodes

- Selected node, dependent nodes, and child nodes

- Selected node and connected nodes

3. After selecting the deployment option, the Generate SQL Script wizard opens. In the wizard, enter details for the following:

Related Topics

- Deploy Workflows using Object Generation Scripts

- Running Workflow Scripts

Show Graph

The Show Graph option opens the Graph Node Editor.

All graphs are displayed in the Graph Node Editor.

Related Topics

- Graph Node Editor

Cut

Use the Cut option to remove the selected object, which could be a node or connection.

You can also delete objects by selecting them and pressing DELETE on your keyboard.

Copy

Use the Copy option to copy one or more nodes and paste them into the same workflow or a different workflow.

To copy and paste nodes:

1. Select the nodes to copy. To select several nodes, hold down the Ctrl key when you click the nodes.

The selected node is highlighted. In this example Classification is selected. The other node is not selected.

2. Right-click and select Copy from the context menu. Alternately, you can press Ctrl+C to copy the selected nodes.

Note:

Copying and pasting nodes do not carry any mining models or results from the original node.

Paste

Use the Paste option to paste the copied object in the workflow.

To paste an object, right-click the workflow and click Paste. Alternately, you can press Ctrl+V.

Note:

Node names and model names are changed to avoid naming collisions. To preserve names, use the option Extended Paste.

Related Topics

- Copy

- Extended Paste

Extended Paste

Use the Extended Paste option to preserve node and model names while pasting them.

The default behavior of Paste is to change node names and model names to avoid naming collisions.

To go to the Extended Paste option, right-click the workflow and click Extended Paste. Alternately, you can press Control+Shift+V.

Note:

If model names are not unique, then the models may be overwritten when they are rebuilt.

Related Topics

- Copy

Select All

Use the Select All option to select all the nodes in a workflow.

The selected nodes and links are highlighted in a dark blue border.

Performance Settings

Use the Performance Settings option to edit Parallel settings and In-Memory settings of the nodes.

If you click Performance Settings in the context menu, or if you click Performance Options in the workflow toolbar, then the Edit Selected Node Settings dialog box opens. It lists all the nodes that comprise the workflow. To edit the settings in the Edit Selected Node Settings dialog box:

- Click Parallel Settings and select:

- Enable: To enable parallel settings in the selected nodes in the workflow.

- Disable: To disable parallel settings in the selected nodes in the workflow.

- All: To turn on parallel processing for all nodes in the workflow.

- None: To turn off parallel processing for all nodes in the workflow.

- Click In-Memory Settings and select:

- Enable: To enable In-Memory settings for the selected nodes in the workflow.

- Disable: To disable In-Memory settings for the selected nodes in the workflow.

- All: To turn on In-Memory settings for the selected nodes in the workflow.

- None: To turn off In-Memory settings for all nodes in the workflow

- Click to set the Degree of Parallel, and In-Memory settings such as Compression Method, and Priority Levels in the Edit Node Performance Settings dialog box.
If you specify parallel settings for at least one node, then this indication appears in the workflow title bar:

Performance Settings is either On for Selected nodes, On (for All nodes), or Off. You can click Performance Options to open the Edit Selected Node Settings dialog box.

- Click to edit default the preferences for parallel processing.

- Edit Node Default Settings: You can edit the Parallel Settings and In-Memory settings for the selected node in the Performance Options dialog box. You can access the Performance Options dialog box from the Preferences options in the SQL Developer Tools menu.

- Change Settings to Default

Related Topics

- About Parallel Processing

- Edit Node Performance Settings

- Performance Options

Toolbar Actions

Use the Toolbar Action option to select actions in the toolbar from the context menu.

Current actions are Zoom In and Zoom Out.

Related Topics

- Managing Workflows using Workflow Controls

Show Runtime Errors

Use the Show Runtime Errors to view errors related to node failure during runtime. This option is displayed only when running of the node fails at runtime.

The Event Log opens with a list of errors. Select the error to see the exact message and details.

Related Topics

- View an Event Log

Show Validation Errors

Use the Show Validation Errors option to view validation errors, if any.

This option is displayed only when there are validation errors. For example, if an Association node is not connected to a Data Source node, then select Show Validation Errors to view the validation error No build data input node connected.

You can also view validation errors by moving the mouse over the node. The errors are displayed in a tool tip.

Save SQL

Use the Save SQL option to generate SQL script for the selected node.

To generate SQL script for the selected node:

1. Right-click the node and click Save SQL.

2. Select any one of the options to save the generated SQL script:

- SQL to Clipboard

- SQL to File

- SQL Script to Clipboard

- SQL Script to File

When you save to a file, the system provides a default location. You can browse to change this location. You can also create a folder for scripts.

The saved SQL includes SQL generated by the current node and all of its parent nodes that are data providers. The SQL lineage ends when it encounters a node that represents persisted objects, such as tables or models.

The generated script does not generate all behavior of the node. The script does not create any objects. For example, if you select Save SQLfor a Create Table node, then it does not generate a script to create the table. Instead, it generates a script to query the created table.

Related Topics

- Deploy Workflows using Data Query Scripts

Validate Parents

Use the Validate Parents option to validate all parent nodes of the current node.

To validate parent nodes of a node, right-click the node and select Validate Parents.

You can validate parent nodes when the node is in Ready, Complete and Error state. All parent nodes must be in completed state.

Compare Test Results

Use the Compare Test Results option to view and compare test results of models that are built successfully.

For Classification and Regression models, this option displays the test results for all successfully built models to allow you to pick the model that best solves the problem.

View Test Results

Use the View Test Results option to view the test results of the selected model. This option is applicable only for Classification and Regression models.

The test results are displayed in the test viewer of the respective models:

Related Topics

- Classification Model Test Viewer

- Regression Model Test Viewer

Go to Properties

Use the Go to Properties option to open the Properties pane of the selected node.

Related Topics

- Managing Workflows and Nodes in the Properties Pane

Navigate

Use the Navigate option to view the links available from the selected node.

Note:
The Navigate option is enabled only if there are links to other nodes.

Navigate displays the collection of links available from this node. Selecting one of the links selects the link and the selected link is highlighted in the workflow. The link itself has context menu options as well so you can right click and continue with the Navigate option. You can also use the arrow keys to progress to the next node.

Create the Table or View Node Properties

In the Properties pane, you can examine and change the characteristics or properties of a node.

To manage the Create Table or View node from Properties pane:

1. Right-click the node and click Go to Properties.

2. The Properties pane opens in the lower right panel in the SQL Developer window. The name of the table or view identifies the Create Table or View property. The Properties pane consists of the following:

- Table

- Columns

- Cache

- Details

- Table
Table displays the name of the Table or View.

- Columns
Columns displays the columns of the table.

- Automatic Behavior
If you select the Auto Input Column Selection option, then columns in the node are added or removed automatically, subject to certain scenarios.

Related Topics

- Performing Tasks from Workflow Context Menu

Table

Table displays the name of the Table or View.

You can perform the following tasks:

- Change the name of the Table or View: If you change the default name of the table, then the name of the node changes to match the name of the table. For example, if you change the default name of the table to PREDICTIONS, then the name of the Create Table or View node also changes to PREDICTIONS.

- Change the object type from table to view: The default type is Table. To create a view, click View.

Columns

Columns displays the columns of the table.

The default setting enables automatic behavior. If you deselect Auto Input Column Selection, then you can manually select and edit the columns in the table. You can:

- Delete columns: Select the column to delete and click .

- Edit columns: Select the column and click . Edit the required changes in:

- Select Column dialog box

- Edit Data Guide dialog box for JSON data

Related Topics

- Automatic Behavior

- Edit Data Guide

- Select Columns

Automatic Behavior

If you select the Auto Input Column Selection option, then columns in the node are added or removed automatically, subject to certain scenarios.

If Auto Input Column Selection is selected, then the possible scenarios are:

- Scenario 1: When input is connected, all columns in the input node are selected. The node becomes valid. It assumes that at least one column is included in the specification.

- Scenario 2: When input is disconnected, all columns are automatically removed. The node becomes invalid.

- Scenario 3: When the input node is edited, the possible edit scenarios are:

- Add column: The column is added to the Create Table or View node, if it is compatible.

- Remove column: The column is removed from the Create Table or View node.

- Edit column: If a column is edited, then any change in the column data type could trigger an invalid state in the node.

Note:

If the Auto Input Column Selection option is deselected, then you must manually add and remove column specifications from the Create Table or View node.

Data Source Node

A Data Source node defines source data for the workflow. For example, a Data Source node specifies the build data for a model.

Any table or view accessible to the user can be selected as the source. This node does not allow any input node connections. Data Source nodes rely on database resources for their definitions. It may be necessary to refresh a node definition if the database resources change. For example, if the resources are deleted or re-created.

Data Source nodes can run in parallel.

Only certain data types are allowed in a Data Source node. Columns with other data types are excluded.

- Supported Data Types for Data Source Nodes
Most of the basic Oracle data types are supported by the Data Source node.

- Support for Date and Time Data
Data and time data types cannot be used for other functions in Oracle Data Miner. In particular, date and time data types cannot be the targets of model builds.

- Working with the Data Source Node
Lists the tasks you can perform using the Data Source nodes.

- Data Source Node Viewer
The Data Viewer contains information related to data, graphs, columns and SQL queries contained in the node.

- Data Source Node Properties
In the Properties pane, you can examine and change the characteristics or properties of a node.

Related Topics

- About Parallel Processing

Supported Data Types for Data Source Nodes

Most of the basic Oracle data types are supported by the Data Source node.

Object-based data types can be included, but each object type has to be well understood. Object data types require storage clauses to be defined at appropriate levels with the object hierarchy.

These data types are fully supported:

- VARCHAR2

- CHAR

- FLOAT

- NUMBER

- CLOB

- NESTED_NUMERICALS

- NESTED_CATEGORICALS

These data types are supported by Oracle Data Mining 12c Release 1 (12.1) and later:

- BINARY_DOUBLE

- BINARY_FLOAT

- DM_NESTED_BINARY_DOUBLES

- DM_NESTED_BINARY_DOUBLES

- BLOB, for Text only

The BINARY data types and BLOB are supported by Oracle Data Miner if you are connected to Oracle Data Mining 12c Release 1 and later.

These data types for date and time are partially supported:

- DATE

- TIMESTAMP

- TIMESTAMP_WITH_LOCAL_TIMEZONE

- TIMESTAMP_WITH_TIMEZONE

- TIMESTAMP_WITH_LOCAL_TIMEZONE

Oracle Database 12.1.0.2 supports JSON data in the following data types. Oracle Data Miner derives pseudo JSON data types from these data types:

- VARCHAR2

- CLOB

- BLOB

- RAW

- NCLOB

- NVARCHAR2

Oracle Data Miner 17.3 supports the following data types for input as columns in a Data Source node, and as new computed columns within the workflow:

- RAW

- ROWID

- UROWID

- URITYPE. The URITYPE data type provides many sub type instances, which are also supported by Oracle Data Miner 17.3. They are:

- HTTPURITYPE

- DBURITYPE

- XDBURITYPE

Related Topics

- Support for Date and Time Data

Support for Date and Time Data

Data and time data types cannot be used for other functions in Oracle Data Miner. In particular, date and time data types cannot be the targets of model builds.

The Transform transformation partially supports the data and time data types DATE, TIMESTAMP, TIMESTAMP_WITH_LOCAL_TIMEZONE, and TIMESTAMP_WITH_TIMEZONE, as follows:

- Attributes with data and time data types can be binned using Equal Width or Custom binning.

- You can apply Statistic or Value missing values treatments to attributes with the data and time data types.

Attributes with data and time types are analyzed by the Explore Data node.

Related Topics

- Transforms Nodes

- Explore Data Node

Working with the Data Source Node

Lists the tasks you can perform using the Data Source nodes.

- Create a Data Source Node
You create a Data Source node after creating a workflow.

- Define a Data Source Node
If you import a workflow that uses tables or views that are not available, then you can use the Define a Data Source Node wizard to define a data source to replace the missing one.

- Edit Data Guide
The Edit Data Guide dialog box allows you to specify how data guide should be generated for a selected JSON type column.

- JSON Settings
In the JSON Parsing Settings dialog box, you can specify node settings that determine how data guides are generated.

- Edit a Data Source Node
You can modify the operation of the Data Source node in the Edit Data Source Node dialog box.

- Running a Data Source Node
To run a valid Data Source node, right-click the node and select Run.

- Data Source Node Context Menu
The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

Create a Data Source Node

You create a Data Source node after creating a workflow.

To create a Data Source node and attach data to it:

1. In the Components pane, go to Workflow Editor. If the Components pane is not visible, then in the SQL Developer menu bar, go to View and click Components. Alternately, press Ctrl+Shift+P to dock the Components pane.

2. In the Data section, click Data Source node icon.

3. Drag and drop the Data Source node from the Components pane to the Workflow pane. This adds the Data Source node to the workflow. The Define Data Source dialog box opens.

4. In the Define Data Source dialog box, you can select a table or view. By default, the tables in your schema are listed. You can add tables from other schemas to which you have access, in the Edit Schema List dialog box.

5. Click Next.

6. In the Define Data Source - Select Columns dialog box, add or remove attributes to the table.

7. Click Finish.

8. In the Select Table window, select the table or view to use. Click OK. The Properties pane displays information about the table or view that you selected. The node can now be run.

The default name of the node is the name of the table or view that you select. If SH.CUSTOMERS is the name of the table, then the node is named CUSTOMERS.

- Edit Schema List
By default, tables and views from the schema to which you are connected, are listed.

Related Topics

- Define a Data Source Node

Edit Schema List

By default, tables and views from the schema to which you are connected, are listed.

To add other schemas:

- If this is the first time that you are adding schemas, then click Add Schemas.

- If you have already added schemas, then click Edit Schemas.

The Edit Schema List dialog box opens. Available Schemas is a list of the schemas to which you have access. To display tables and views in one of these schemas, move the name to Selected Schemas.

For example, to display tables in the SH schema, move SH to the Selected Schemas list. Click OK.

You return to the Define Data Source wizard. Select Include Tables from Other Schemas. Tables and views in the added schemas are listed. For example, if you selected SH, then you can see tables, such as SH.CUSTOMERS, in the Available Tables/Views list. You can select these tables as data sources.

Define a Data Source Node

If you import a workflow that uses tables or views that are not available, then you can use the Define a Data Source Node wizard to define a data source to replace the missing one.

The wizard also detects input columns with JSON data types. You can use this interface to:

- Define table and attributes for a new Data Source node.

- Edit an existing data source node.

The wizard has two steps:

1. Select Table: Here, the tables and views to which you have access, are displayed. The schemas are the schemas to which you are connected, along with the schemas added using the Edit Schema List. Select the table or view types. The columns and data are displayed in the lower pane in the following tabs:

- Columns: Lists the columns of the selected table in a grid. For each column, Data Type, Mining Type, Length, Precision, Scale, and Column ID are displayed.

- Data: Displays the data in the table arranged according to Column ID.

- To include all attributes in the table, click Finish.

- To manually exclude certain attributes in the table, click Next.

- To change the table selection for an existing node, click Edit Data Source Node.

2. Select Columns: By default, the Define Data Source wizard includes all columns in the Table or View. You can perform the following tasks:

- Include a column by moving the attribute from Available Attribute to Selected Attributes section.
A drop-down list is available for JSON data. If there is an input table with JSON data, then the wizard detects the column as a JSON column and displays it in the Data Type column. If the wizard cannot detect JSON data, then you can manually change the data type for the column by clicking the drop-down list.

- Specify Data Guide Settings: Applicable only for JSON data type. Select the attribute and click to specify Data Guide settings in the Edit Data Guide dialog box.

- JSON Settings: Click JSON Settings to specify node settings that determines how Data Guides are generated. This option is applicable only for JSON data.

Related Topics

- Edit a Data Source Node

- Edit Schema List

- Edit Data Guide

- JSON Settings

Edit Data Guide

The Edit Data Guide dialog box allows you to specify how data guide should be generated for a selected JSON type column.

The Edit Data Guide dialog box has two tabs:

- Structure: Displays the JSON data structure for the selected column

- Data: Displays the content of the imported or generated Data Guide table

You can perform the following tasks:

- Generate Data Guide: You can generate a new data guide table whenever the node is run or rerun. To generate a data guide, select any of the following options from the Data Guide Generation drop-down list:

- Default: If the option Generate Data Guide if necessary in the JSON Settings dialog box is selected, then the data guide table is generated whenever the node is run or rerun. Otherwise, no data guide is generated.

- On: Select this option to generate a new data guide table whenever the node is run or rerun.

- Off: Select this option to not generate a new data guide table whenever the node is run or rerun.

- Import from Workflow: Select this option to import a data guide from an existing JSON type column defined in a Data Source node or Create Table node within the same workflow or from a different workflow using the Select Data Guide dialog box.

- Import from file: Select this option to import a data guide from a CSV file. The process validates the data guide, that is, it verifies the correctness of the column header, JSON path format, JSON types, and so on during an import operation.

- Remove Data Guide: To delete the current Data Guide table, click .

- Export Data Guide: Select this option to export the current data guide table to a CSV file. If you find the generated data guide does not fully represent the underlying JSON data, then you have the option to export the data guide and add any missing JSON paths. You can then import the data guide back to replace the generated one.

- Select Data Guide
The Select Data Guide dialog box enables you to import a data guide table from an existing JSON type column defined in a Data Source node or Create Table node within the same workflow or from a different workflow.

Related Topics

- JSON Settings

Select Data Guide

The Select Data Guide dialog box enables you to import a data guide table from an existing JSON type column defined in a Data Source node or Create Table node within the same workflow or from a different workflow.

Only completed nodes with generated JSON schema are displayed. To import a data guide table:

1. In the Show field, select a workflow from the drop-down list.

2. Select the nodes to import.

3. Click OK.

JSON Settings

In the JSON Parsing Settings dialog box, you can specify node settings that determine how data guides are generated.

A data guide is used whenever a JSON structure is present in the UI, for example, JSON Query node. Because the data guide table generation could be time consuming, especially for large JSON data, the following settings offers some control on the table generation:

- Generate Data Guide if necessary: By default, this option is selected. It generates a data guide for the JSON type columns. Deselect this option if JSON data is not used in the product. Therefore, no Data Guide will be generated.

- Sampling: Defines how many JSON documents stored in a column are to be processed, to generate the data guide table. The JSON document contains the entire content of the JSON column for a given row.

- Max number of documents: 2000 (Default). Use the arrows to change this setting.

- Limit Document Values to Process: Defines how many JSON values (Number, String, Boolean) are to be parsed in the document to generate the data guide table.

- Max number per document: 10,000 (Default). Use the arrows to change this setting.

Edit a Data Source Node

You can modify the operation of the Data Source node in the Edit Data Source Node dialog box.

To edit an existing Data Source node:

1. Double-click or right-click the node and select Edit. The Edit Data Source Node opens.

2. In the Edit Data Source Node dialog box, you can change the attributes selected in the current Data Source. You can perform the following tasks:

- Change attribute selection: To change the attribute selection, move the attributes from the Available Attributes pane to the Selected Attributes pane by using the arrows. For example, to remove ATTRIBUTE1 from the data source, move ATTRIBUTE1 from the Selected Attributes list to the Available Attributes list. After you are done, click OK.

- Select a different table: To select a different table or view, click Edit. The Define Data Source dialog box opens.

Related Topics

- Define a Data Source Node

Running a Data Source Node

To run a valid Data Source node, right-click the node and select Run.

The Oracle Data Miner server generates a sample of the selected table or view. The size of the table and type of sampling used is determined by the sample settings of the node.

If the node is complete but is required to provide data to a child node that is being run, then the node is run for validation to ensure that the columns and table still exist. If there are any errors, then the node state is set to Error and affected attributes are set to Invalid.

Data Source Node Context Menu

The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

Right-click the Data Source node. The following options are available in the context-menu:

- Connect

- Edit: Opens the Edit a Data Source Node dialog box.

- Attributes: Opens the Select Attributes dialog box.

- Define a Data Source Node

- Run

- Force Run

- View Data: Opens the Data Source Node Viewer dialog box.

- Deploy

- Show Graph

- Generate Apply Chain

- Refresh Input Data Definition

- Cut

- Copy

- Paste

- Extended Paste

- Select All

- Performance Settings. This opens the Edit Selected Node Settings dialog box, where you can set Parallel Settings and In-Memory settings for the node.

- Show Event Log

- Show Runtime Errors. Displayed only if there is an error.

- Show Validation Errors. Displayed only if there are validation errors.

- Go to Properties

- Navigate

- Select Attributes
The Select Attributes dialog box enables you to move attributes between the Available Attributes list and the Selected Attributes list.

Select Attributes

The Select Attributes dialog box enables you to move attributes between the Available Attributes list and the Selected Attributes list.

To deselect an attribute, move it to the Available Attributes list.

You can search the Available Attributes list for attributes.

Use the shuttle controls to move attributes between the lists.

When you have finished selecting attributes, click OK.

Data Source Node Viewer

The Data Viewer contains information related to data, graphs, columns and SQL queries contained in the node.

To view data, right-click the node and select View Data from the context menu. The Data Viewer opens.

Note:

You can view data only when the Data Source node is in the Valid state.

The data viewer has these tabs:

- Data
The Data tab displays a sample of the data.

- Graph
In the Graph tab, you can create graphs based on the numeric data.

- Columns
The Column tab is a list of all the columns that are output from the node.

- SQL
The SQL tab provides the SQL Details text area. The text area displays the SQL code that generates the data provided by the actual view shown in the Data tab.

Data

The Data tab displays a sample of the data.

The Data Viewer provides a grid display of the rows of data either from the sampling, or pulled through the lineage of nodes back to the source tables.

The Data tab for MINING_DATA_BUILD_V:

You can perform the following tasks:

- Refresh: Click to refresh the data.

- View: Click View and select either Actual Data or Cached Data. Cached data is available only if you cache data in the Cache section of the Properties pane.

- Sort: Click Sort to sort data according to applicable criteria. Displays the Select Column to Sort By dialog box.

- Filter: In the Filter field, enter a WHERE clause to select data.

- Select Column to Sort By

Related Topics

- Cache

Select Column to Sort By

This dialog box enables you to:

- Select multiple columns to sort

- Determine the column ordering

- Determine ascending or descending order by column

- Specify Nulls First so that null values appear before real data values

The sort order is preserved until you clear it.

Column headers are also sort-enabled to provide a temporary override to the sort settings.

Graph

In the Graph tab, you can create graphs based on the numeric data.

Related Topics

- Graph Node

Columns

The Column tab is a list of all the columns that are output from the node.

For each column, the Name, Data Type, Mining Type, Length, Precision and Scale (for floating point), and Column ID are displayed.

- If the node has not run, then the table or view structure provided by the database is displayed.

- If the node has run successfully, then the structure of the sample table is displayed, based on the sampling defined when the node was specified.

There are several filtering options that limit the columns displayed. The filter settings with the (or)/(and) suffixes allow you to enter multiple strings separated by spaces. For example, if the Name/Data Type/Mining Type(or) is selected, then the filter string A B produces all columns where the name or the data type or the mining type starts with the letter A or B.

SQL

The SQL tab provides the SQL Details text area. The text area displays the SQL code that generates the data provided by the actual view shown in the Data tab.

The SQL can be a stacked expression that includes SQL from parent nodes, depending on what lineage is required to access the actual data.

You can copy the SQL and run it in a suitable SQL interface. Select All (Ctrl+A) and Copy (Ctrl+C) are enabled.

The Search control is a standard search control that highlights matching text and searches forward and backward.

Data Source Node Properties

In the Properties pane, you can examine and change the characteristics or properties of a node.

To view the properties of a node, click the node and click Properties. If the Properties pane is closed, then go to View and click Properties. Alternately, right-click the node and click Go to Properties. The Properties pane for a Data Source node has these sections:

- Data
The Data section in Properties displays information related to the data and the data source, which can be a table or view.

- Cache
The Cache section provides the option to generate a cache for output data. You can change this default using the transform preference.

- Details
The Details section displays the name of the node and any comments about it.

Related Topics

- Properties

Data

The Data section in Properties displays information related to the data and the data source, which can be a table or view.

The Data section consists of the following:

- Source Table: Displays the name of the source table or view for the Data Source node. If no source table is associated with the node, then click … to the right of Source Table. A list of tables and views that are accessible from the data mining account is displayed. You can select the table or view. You can also use this process to change the table or view.

- Data: Displays the attributes in a grid. For each attribute, the name, the alias, and the data type are displayed. You can perform the following tasks:

- Create an alias for an attributed by entering the alias in the appropriate cell.

- Filter attributes.

- Delete attributes. Select the attribute and click .

- Edit attributes. Select the attribute and click .

- Select Attributes to include in the Data Source.

- Refresh the node. Click .

Related Topics

- Filter

- Select Attributes

Cache

The Cache section provides the option to generate a cache for output data. You can change this default using the transform preference.

You can perform the following tasks:

- Generate Cache of Output Data to Optimize Viewing of Results: Select this option to generate a cache. The default setting is to not generate a cache.

- Sampling Size: You can select caching or override the default settings. Default sampling size is Number of Rows Default Value=2000

Related Topics

- Transforms

Details

The Details section displays the name of the node and any comments about it.

You can change the name and comments in the fields here:

- Node Name

- Node Comments

Explore Data Node

The Explore Data node provides the profile of any input data source. Explore Data Statistics can either be based on all data or on a sample of the data.

The Explore Data node enables you to do the following:

- View common statistics and a histogram for each column. Optionally, you can select a Group By attribute and have multivariate view of histograms generated.

- Generate an output flow containing the results of all the statistical analysis. You can connect any source of data input to an Explore Data node. For example, you can attach an Explore Data node to an Apply node.

- Analyze attributes with all supported data types and the following data types for date and time:

- DATE

- TIMESTAMP

- TIMESTAMP_WITH_TIMEZONE

- TIMESTAMP_WITH_LOCAL_TIMEZONE

- Run Explore Data node in parallel.

- Create graphs.

- Export the statistic generated by the Explore Data node using SQL Developer.

- Create an Explore Data Node
You create an Explore Data node and connect it to a data source to analyze the data in the data source. You can connect an Explore Data node to any data source.

- Edit the Explore Data Node
You can view and edit information related to inputs and statistics in the Edit Explore Data Node dialog box.

- Explore Data Node Viewer
The Explore Data Node viewer displays the statistics and other analyses performed by the Explore Data node.

- Export Node Calculations
When an Explore node runs, it writes the statistics that it calculates to a database table. You can export the statistics calculated by the Explore node to a Microsoft Excel spreadsheet.

- Perform Tasks from the Explore Data Node Context Menu
The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

- Explore Data Node Properties
In the Properties pane, you can examine and change the characteristics or properties of a node.

Related Topics

- About Parallel Processing

- Graph Node

Create an Explore Data Node

You create an Explore Data node and connect it to a data source to analyze the data in the data source. You can connect an Explore Data node to any data source.

To create an Explore Data node:

1. In the Components pane, go to Workflow Editor. If the Components pane is not visible, then in the SQL Developer menu bar, go to View and click Components. Alternately, press Ctrl+Shift+P to dock the Components pane.

2. In the Data section, click Explore Data.

3. Drag and drop the node from the Components pane to the Workflow pane.
The node is added to the workflow. The GUI shows that the node has no data associated with it. Therefore, it cannot be run.

4. Right-click the node from which to create the table, and click Connect in the context menu.

5. Draw a line from the node to analyze to the Explore Data node and click again.

6. To generate statistics and analyze data, right-click the Explore Data node, and click Run.

7. After running the node, right-click the node and click View Data. The data is displayed in the Explore Data Node Data Viewer.

By default, all attributes in the data source are analyzed. You can specify specific attributes to analyze.

Related Topics

- Explore Data Node Viewer

- Edit the Explore Data Node

Edit the Explore Data Node

You can view and edit information related to inputs and statistics in the Edit Explore Data Node dialog box.

You can edit the Explore Data node by either double-clicking the node or by right-clicking the node and selecting Edit.

The Edit Explore Data Node editor has these tabs:

- Input (Explore)
In the Input tab, you can specify the attributes to analyze. By default, all attributes in the data source are analyzed.

- Statistics (Explore)
The statistics tab contains a list of statistics available. For each statistic, there is a brief definition and an indication of the cost to calculate that statistic.

Input (Explore)

In the Input tab, you can specify the attributes to analyze. By default, all attributes in the data source are analyzed.

The default is to select no Group By attribute. Group By displays a sorted list of attributes that are limited to data types that can be binned by Explore Data. Selecting a Group By attribute enables you to analyze data based on the selected attribute. For example, suppose that the data contains AGE and GENDER as attributes. If you select AGE as the Group By attribute, then the histograms for GENDER show the age composition of each value of GENDER.

Change the list of attributes to analyze in either of these ways

- Right-click the Explore Data node and select Edit. The Select Attributes dialog box opens.

- Select the Explore Data node. The Explore tab in the Explore Data node Properties pane enables you to change the list of attributes.

- Select Attributes
In the Select Attribute dialog box, you can choose the attributes of the data source for which you want to view the statistics. By default, all attributes of the data source are selected.

Related Topics

- Explore Data Node Properties

Select Attributes

In the Select Attribute dialog box, you can choose the attributes of the data source for which you want to view the statistics. By default, all attributes of the data source are selected.

If you do not want to view statistics for an attribute, then move it from the Selected Attributes list to the Available Attributes list.

You can sort the lists by Name or by Data Type.

When you have finished, click OK.

Statistics (Explore)

The statistics tab contains a list of statistics available. For each statistic, there is a brief definition and an indication of the cost to calculate that statistic.

Click the Statistics tab to specify the statistics to calculate. You can also change statistics in the Statistics section of Explore node Properties pane.

If you change any of the default selections, then click Restore Defaults to change all selections to the default selection.

You can search for an individual statistic by name.

By default, Oracle Data Miner calculates these statistics:

- Average

- Distinct percent

- Maximum

- Median

- Minimum

- Mode sampled

- Percent NULLs

- Standard deviation

- Variance

The default statistics have a low or medium cost to calculate.

Skewness and kurtosis have a high cost to calculate. They are not selected by default. You can select them if necessary.

Calculating mode using a sample is a low cost operation. Calculating the mode using all available data is a very high cost operation. To calculate mode using sample data or all available data, click Mode (Sampled) in the Edit Explore Data Node dialog box.

- Mode (Sampled)

Related Topics

- Export Node Calculations

Mode (Sampled)

The default is to calculate mode using a sample of 2000 records. You can calculate the mode using all available data. This calculation has a very high cost.

To use all available data, click the Available Data option and click OK.

Explore Data Node Viewer

The Explore Data Node viewer displays the statistics and other analyses performed by the Explore Data node.

After the node runs successfully, you can view the data in the Explore Data Node viewer.

To view the data, right-click the node and select View Data. The viewer opens in a new tab.

The viewer displays the information in the following tabs:

- Statistics
The Statistics tab displays the statistics calculated by the Explore Data node.

- Columns
The Column tab is a list of all the columns that are output from the node.

- Data
The Data section in Properties displays information related to the data and the data source, which can be a table or view.

- SQL
The SQL tab provides the SQL Details text area. The text area displays the SQL code that generates the data provided by the actual view shown in the Data tab.

Statistics

The Statistics tab displays the statistics calculated by the Explore Data node.

Attributes are listed the Statistics grid. For each attribute, the name, data type, histogram, and summary of the statistics are displayed.

To view a large version of the histogram for an attribute, select the attribute. The histogram is displayed below the grid. If the attribute has null values, then the histogram has a separate bin labeled Null bin. The histogram may also contain an Others bin consisting of values that are not NULL but are not in any other bin.

For more information about the histogram, go to the large histogram and right-click the attribute name. These choices are available:

- Copy to Clipboard: Copies the histogram to the Microsoft Windows clipboard. You can paste this histogram in to any rich editor, such as a word processor or image editor.

- Save Image As: Exports the image to a PNG file that can be stored in the file system.

- View Data: Displays the data used to create the histogram in a pop up window. You can search for attribute name, attribute value, or attribute percent. Click Close when you have finished.

Histograms created by Oracle Data Miner use a sample of the data and can be slightly different each time they are displayed.

For all data types, a histogram is created. The number of distinct values, the percentage of NULL values, and the number of distinct values are displayed.

Additional statistics calculated depend on the data type of the attribute:

- For character attributes VARCHAR2, the Mode is calculated.

- For numeric attributes NUMBER or FLOAT,the following are calculated:

- Average

- Minimum value

- Maximum value

- Standard deviation

- Skewness (a measure of the asymmetry of the distribution)

- Kurtosis (a measure of flatness or peakedness of the curve describing a frequency distribution in the region about its mode)

Columns

The Column tab is a list of all the columns that are output from the node.

For each column, the Name, Data Type, Mining Type, Length, Precision and Scale (for floating point), and Column ID are displayed.

- If the node has not run, then the table or view structure provided by the database is displayed.

- If the node has run successfully, then the structure of the sample table is displayed, based on the sampling defined when the node was specified.

There are several filtering options that limit the columns displayed. The filter settings with the (or)/(and) suffixes allow you to enter multiple strings separated by spaces. For example, if the Name/Data Type/Mining Type(or) is selected, then the filter string A B produces all columns where the name or the data type or the mining type starts with the letter A or B.

Data

The Data section in Properties displays information related to the data and the data source, which can be a table or view.

The Data section consists of the following:

- Source Table: Displays the name of the source table or view for the Data Source node. If no source table is associated with the node, then click … to the right of Source Table. A list of tables and views that are accessible from the data mining account is displayed. You can select the table or view. You can also use this process to change the table or view.

- Data: Displays the attributes in a grid. For each attribute, the name, the alias, and the data type are displayed. You can perform the following tasks:

- Create an alias for an attributed by entering the alias in the appropriate cell.

- Filter attributes.

- Delete attributes. Select the attribute and click .

- Edit attributes. Select the attribute and click .

- Select Attributes to include in the Data Source.

- Refresh the node. Click .

Related Topics

- Filter

- Select Attributes

SQL

The SQL tab provides the SQL Details text area. The text area displays the SQL code that generates the data provided by the actual view shown in the Data tab.

The SQL can be a stacked expression that includes SQL from parent nodes, depending on what lineage is required to access the actual data.

You can copy the SQL and run it in a suitable SQL interface. Select All (Ctrl+A) and Copy (Ctrl+C) are enabled.

The Search control is a standard search control that highlights matching text and searches forward and backward.

Export Node Calculations

When an Explore node runs, it writes the statistics that it calculates to a database table. You can export the statistics calculated by the Explore node to a Microsoft Excel spreadsheet.

To export the statistics:

1. When an Explore node runs, it writes the statistics that it calculates to a database table. The name of the table is in the Output section of the Explore Data node Properties pane. Suppose that the name of the table is OUTPUT_8_3. If the Properties pane of the node is not visible, then right-click the Explore node and select Go to Properties.

2. In SQL Developer, go to the Connections tab. Expand the connection that you used for data mining.

3. Select Export in the context menu. The Export Wizard opens.

4. In the Export Wizard:

- Deselect Export DDL.

- In the Export Data section, select the version of Microsoft Excel to which you want to export from the Format drop-down list.

- Specify a file name. Alternately, you can accept the default.

- Click Next.

5. Click Finish. SQL Developer exports the table to the spreadsheet.

The spreadsheet contains the statistics. It contains the names of the histograms generated by the Explore Data node.

To export an individual histogram, right-click the histogram and save the graphic.

Perform Tasks from the Explore Data Node Context Menu

The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

The context menu for an Explore Data node has these selections:

- Connect

- Edit: Opens the Select Attributes dialog box.

- Validate Parents

- Run

- Force Run

- View Data: Opens the Data Source Node Viewer dialog box.

- Deploy

- Show Graph

- Generate Apply Chain

- Cut

- Copy

- Paste

- Extended Paste

- Select All

- Performance Settings. This opens the Edit Selected Node Settings dialog box, where you can set Parallel Settings and In-Memory settings for the node.

- Show Event Log

- Show Runtime Errors. Displayed only if there is an error.

- Show Validation Errors. Displayed only if there are validation errors.

- Go to Properties

- Navigate

Related Topics

- Select Attributes

- Data Source Node Viewer

Explore Data Node Properties

In the Properties pane, you can examine and change the characteristics or properties of a node.

To view the properties of a node, click the node and click Properties. If the Properties pane is closed, then go to View and click Properties. Alternately, right-click the node and click Go to Properties.

The Explore Data node Properties pane has these sections:

- Input (Properties)
The Input section lists the attributes that are analyzed.

- Statistics
The Statistics section lists the calculated statistics.

- Output
The Output section lists the columns in the data source.

- Histogram
Use bins to create histograms. The Histogram tab lists the default number of bins.

- Sample
The data is sampled to support data analysis.

Input (Properties)

The Input section lists the attributes that are analyzed.

Attributes are listed in a grid. For each attribute, the name and the data type are displayed. To sort the list of attributes by name or data type, click the heading in the grid.

You can specify a Group By attribute. Select an attribute from the list.

You can either choose the default settings or select columns:

- To select columns, deselect Auto Input Columns Selection.

- To delete an attribute, select it and click .

- To edit an attribute, click .

The Select Attributes dialog box opens.

Related Topics

- Select Attributes

Statistics

The Statistics section lists the calculated statistics.

Related Topics

- Statistics (Explore)

Output

The Output section lists the columns in the data source.

The names and data type of each column is listed in a grid. You can search the grid by name (the default) or by data type.

To clear the search, click .

Histogram

Use bins to create histograms. The Histogram tab lists the default number of bins.

The default number of bins for the following types of bins:

- Numerical Bins

- Categorical Bins

- Date Bins

- Null Values

- Other Values

The default number of bins for all of these bin types is 10. The default specifies a maximum number of bins.

Sample

The data is sampled to support data analysis.

The default is to use a sample. The Sample tab has the following selections:

- Use All Data: By default, Use All Data is deselected.

- Sampling Size: The default is Number of Rows with a value of 2000. You can change sampling size to Percent. The default is 60 percent.

Graph Node

A Graph node creates a two-dimensional graph of numeric data.

A Graph node is not a data provider, which means that it cannot be connected to another node.

Note:

You cannot generate code from a Graph node.

You can perform the following tasks:

- Create and edit one or more graphs of different types such as line, scatter, bar, histogram, and box.

- Create graphs with actual data and sample data.

- Run a Graph node in parallel.

- Types of Graphs
Graph node enables you to create two-dimensional graphs of numeric data in several ways.

- Supported Data Types for Graph Nodes
Graph nodes support NUMBER, FLOAT, DATE, AND TIMESTAMP data types.

- Graph Node Context Menu
The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

- Create a Graph Node
You can create a Graph node to visualize numeric data and relationships between numeric variables.

- New Graph
The New Graph dialog box defines a graph with a default name.

- Graph Node Editor
The Graph Node Editor displays all defined graphs. You can modify an existing graph or add graphs to the node.

- Edit Graph
You can edit a graph to change it current attributes and to specify a different graph type.

- Graph Node Properties
Graph node properties is identified by the node name that you are viewing.In the Properties pane, you can examine and change the characteristics or properties of a node.

Related Topics

- About Parallel Processing

Types of Graphs

Graph node enables you to create two-dimensional graphs of numeric data in several ways.

You can create the following types of graphs:

- Line Plot: Uses a line to connect the data points. Line plots are useful for identifying if two variable are correlated. Oracle Data Miner supports two-dimensional line plots.

- Bar Plot: Compares values. The height of the bar represents the measured value or frequency.

- Histogram: Shows frequencies, as adjacent rectangles, erected over discrete intervals (bins), with an area equal to the frequency of the observations in the interval.

- Scatter Plot: Display values in two discrete sets. One variable determines the position on the horizontal axis and the other variable determines the position on the vertical axis.

- Box Plot or Box Graph: Graphically depicts groups of numeric data using the quantiles of the data.

Graph nodes require you to specify one or more axes. Axes must consists of numeric data.

- Box Plot or Box Graph
A box plot graphically depicts groups of numeric data using the quantiles of the data.

Related Topics

- Supported Data Types for Graph Nodes

Box Plot or Box Graph

A box plot graphically depicts groups of numeric data using the quantiles of the data.

The bottom and top of the box are the first and third quartiles, and the band inside the box is the second quartile (the median). In the type of box plot created by Data Miner, the whiskers show the minimum and maximum values of all the data.

Supported Data Types for Graph Nodes

Graph nodes support NUMBER, FLOAT, DATE, AND TIMESTAMP data types.

The supported data types:

- NUMBER

- FLOAT

- DATE

- TIMESTAMP

- TIMESTAMP_WITH_TIMEZONE

- TIMESTAMP_WITH_LOCAL_TIMEZONE

Graph Node Context Menu

The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

To view the context menu, right-click a Graph node. The context menu provides the following options:

- Connect

- Edit. Opens the Edit a Data Source Node dialog box.

- Validate Parents

- Run.

- Force Run

- Save SQL: This option is disabled. It indicates that you cannot generate SQL query for this node.

- Deploy

- Show Graph

- Generate Apply Chain

- Cut

- Copy

- Paste

- Extended Paste

- Select All

- Performance Settings. This opens the Edit Selected Node Settings dialog box, where you can set Parallel Settings and In-Memory settings for the node.

- Show Event Log

- Show Validation Errors. Displayed only if there is an error.

- Show Validation Errors. Displayed if there are validation errors.

- Go to Properties

- Navigate

- Running Graph Node
Run the Graph node to generate sample data.

- Show Graph
The Show Graph option opens the Graph Node Editor.

Related Topics

- Edit a Data Source Node

Running Graph Node

Run the Graph node to generate sample data.

To run the Graph node, right-click the Graph node and click Run.If you do not generate sample data, then graphs are created using the data provided to the node.

Note:

If you import workflow that contains Graph nodes, then you must run the Graph node to view the graphs.

Show Graph

The Show Graph option opens the Graph Node Editor.

All graphs are displayed in the Graph Node Editor.

Related Topics

- Graph Node Editor

Create a Graph Node

You can create a Graph node to visualize numeric data and relationships between numeric variables.

To create a Graph node:

1. In the Components pane, go to Workflow Editor. If the Components pane is not visible, then in the SQL Developer menu bar, go to View and click Components. Alternately, press Ctrl+Shift+P to dock the Components pane.

2. In the Workflow Editor, expand Data and click Graph.

3. Drag and drop the node from the Components pane to the Workflow pane.
The node is added to the workflow. The GUI shows that the node has no data associated with it. Therefore, it cannot be run.

4. Right-click the node from which to create the table, and click Connect in the context menu.

5. Draw a line to the Graph node and click again.

6. To create a graph using sample data, then right-click the graph node and click Run.

7. Double-click the Graph node or right-click the node and select Edit from the context menu.

- If no graphs are defined, then the New Graph dialog box opens. You can define a graph here.

- If a graph is defined, then in the Graph Node Editor, you can add new graphs or edit existing graphs.

8. After running of the graph node is complete, the graph is automatically displayed in the Graph Node Editor. You can edit the graph. You can also define new graphs, modify settings and attributes of graphs, and delete graphs.

Related Topics

- New Graph

- Graph Node Editor

New Graph

The New Graph dialog box defines a graph with a default name.

You can change the name of the graph here.

Select the type of graph to create, and follow the steps to define it.

The graph that you define is displayed in a container. After you have created a graph, you can edit the definition. You can also add graphs, delete graphs, view the data used to construct a graph, and save the graph as a graphic.

- Line or Scatter
Line charts display continuous data over time that is set against a common scale. Scatter charts are commonly used for displaying and comparing numeric values, such as scientific, statistical, and engineering data.

- Bar
A bar graph plots the values of a selected attribute (x-axis) against frequency counts (y-Axis).

- Histogram
A histogram plots the values of a selected attribute along the x-axis against frequency counts along the y-axis.

- Box
A box plot summarizes binned data of a selected attribute (X axis). It graphically depicts groups of numeric data using the quantiles of the data.

- Settings (Graph Node)
You can provide settings specific to the data type of the selected attribute in the graph.

Related Topics

- Graph Node Editor

- Supported Data Types for Graph Nodes

Line or Scatter

Line charts display continuous data over time that is set against a common scale. Scatter charts are commonly used for displaying and comparing numeric values, such as scientific, statistical, and engineering data.

To create a Line or Scatter:

1. Click Line to create a Line graph. The Line graph is the default type. To create scatter graph, click Scatter.

2. For a line graph or a scatter graph, enter the following details:

- Title: This is the title of the graph. You can use the default name or you can provide a different name.

- Comment: Type a description of the graph. This is an optional field.

- For line graph settings, specify the following information:

- X-Axis: Select an attribute for the x-axis of the graph.

- Y-Axis: Select an attribute for the y-axis of the graph.

- Group By: Click this option to select an optional Group By attribute. Use this option to create a series based on the Group By attribute values. Select an attribute for Group By. To specify how the grouping is performed, click Settings.

3. Click OK.

Bar

A bar graph plots the values of a selected attribute (x-axis) against frequency counts (y-Axis).

To specify a bar graph:

1. Click Bar.

2. Specify the following information for Bar Graph settings:

- Title: This is the name of the graph. You can use the default name or you can enter a different name.

- Comment: Provide a description for the bar graph. This is an optional field.

- Specify the following Bar Graph settings:

- X-Axis: Select one attribute to be plotted along the x-axis of the graph. This attribute is handled using either Top N, or Binned (the default). To specify handling, click Settings.

- Y-Axis: Select one attribute to be plotted along the y-axis of the graph. Specify the value for frequency count as statistic. For example, Average, Min, Max Median, Count. The statistic that you choose summarizes the values in the bin.

- Group By: Click this option to select an optional Group By attribute. This attribute groups the values in the bin (stack the bar). Select an attribute for Group By. To specify how the grouping is performed, click Settings.

3. Click OK.

Related Topics

- Settings (Graph Node)

Histogram

A histogram plots the values of a selected attribute along the x-axis against frequency counts along the y-axis.

To create a histogram:

1. Click Histogram.

2. Enter the following information for the histogram:

- Title: This is the name of the histogram. You can use the default name or you can enter a different name.

- Comment: Enter a description of the histogram. This is an optional field.

- Specify the following settings for the Histogram:

- X-Axis: Select an attribute to plot along the x-axis of the graph. This attribute is handled using either Top N, or Binned (the default). To specify handling, click Settings.

- Group By: Click Group By to select an optional Group By attribute. This attribute groups values in the bin (stack the bar). To specify how Group By is performed, click Settings. By default, a histogram with a Group By attribute is displayed as a Stacked Bar Graph. Click , the Stacked Bar Graph icon, and select the Dual Y Bar Graph icon. If you specify an invalid value, then a message appears explaining the problem.

3. Click OK.

Related Topics

- Settings (Graph Node)

Box

A box plot summarizes binned data of a selected attribute (X axis). It graphically depicts groups of numeric data using the quantiles of the data.

The bottom and top of the box are the first and third quartiles, and the band inside the box is the second quartile (the median). In the type of box plot created by Data Miner, the whiskers show the minimum and maximum values of all the data.

To specify a box plot or box graph:

1. Click Box.

2. Enter the following details of the Box Graph:

- Title: This is the name of the Box Graph. You can use the default name or you can enter a different name.

- Comment: Enter a description of the Box Graph. This is an optional field.

- Specify the following settings for the box graph:

- X-Axis: Select an attribute to be plotted along the x-axis of the graph. This attribute is binned using either Top N, or Binned (the default). To specify handling, click Settings.

- Group By: Click Group By to select an optional group by attribute. This attribute provides values on the y-axis. To specify how the grouping is performed, click Settings. If you specify an invalid value, a message appears explaining the problem.

3. Click OK.

Related Topics

- Settings (Graph Node)

Settings (Graph Node)

You can provide settings specific to the data type of the selected attribute in the graph.

Depending on the data type of the attribute selected, one of these dialog boxes is displayed when you click Settings.

- Select Values to Display
In the Select Values to Display dialog box, you can provide specific attribute values when defining a graph.

- Axis Treatment
If you specify a numeric attribute for an axis or Group By attribute, then click Settings to display the Axis Treatment Settings dialog box.

Select Values to Display

In the Select Values to Display dialog box, you can provide specific attribute values when defining a graph.

If you specify a categorical attribute for an axis or the Group By attribute, then click Settings to display the Select Values to Display dialog box.

The values of the attribute are listed in the Values column. Select values in one of the following:

- All

- None

- Default
By default, the most frequent values using Top N is selected.

- Select specific values by clicking the check box for the values.

You can search for values using the search box.

When you have finished, click OK to return to the definition of the graph.

Axis Treatment

If you specify a numeric attribute for an axis or Group By attribute, then click Settings to display the Axis Treatment Settings dialog box.

The options in the Axis Treatment Settings dialog box are:

- Raw Values (as is)

- Binned Automatically: Use Equal width binning for the axis values. The default number of bins is 10. You can change this value. The default is to not show null values. To show null values, click the check box.

Click OK to return to the definition of the graph.

Graph Node Editor

The Graph Node Editor displays all defined graphs. You can modify an existing graph or add graphs to the node.

If at least one graph is defined for a Graph node, then double-click the Graph node to open the Graph Node Editor. If no graphs are defined, then the New Graph dialog box opens.

Each graph is in a container. The graph containers are laid out in a grid.

You can also zoom in to view details of a graph.

These icons are applicable to all graphs in the node:

- To add a new graph, click to open the New Graph dialog box.

- To refresh the display, click .

- To select data used to create the graph, click View. The options are:

- Actual Data: Displays the default unless you have run the Graph node. In this case, the node creates the graph using whatever data is provided.

- Sample Data: Available only if you have run the Graph node to generate sample data.

The name of the new or existing graph is displayed at the top of the container for the graph, along with these controls:

- To adjust the size of the graph (zoom in and out), click .

- To edit the current graph, click . When you edit or add a graph, the results are automatically displayed in the editor.

- To delete the current graph, click .

- To examine specific values of the graph, zoom the graph.

- Zoom Graph
In the Graph node editor, you have the option to zoom in on selected values to view details.

- Viewing Data used to Create Graph
In the Graph node editor, you have the option to save the graph as a graphic.

Related Topics

- New Graph

Zoom Graph

In the Graph node editor, you have the option to zoom in on selected values to view details.

To examine details of a graph, zoom in on the selected values. For example, to see more detail for selected x-axis values, draw a selection box with the mouse that encloses the values. The display shows values in the selection box in more detail and the axis expands. You can zoom in multiple times. When you have finished viewing the selected values, click the graph once for each time that you zoomed in to return to the original graph.

Viewing Data used to Create Graph

In the Graph node editor, you have the option to save the graph as a graphic.

To view the data used to create the graph or to save the graph as a graphic:

1. Right-click the graph.

2. Select any one of the following options:

- Copy to Clipboard: Copies the graph to the Microsoft Windows clipboard

- Save Image As: Saves the graph in a PNG file format

- View Data: Displays the data used to create the graph

Edit Graph

You can edit a graph to change it current attributes and to specify a different graph type.

When you edit a graph, you can do the following:

- Change the attributes of the existing graph type. For example, if you edit a line graph, then you can change the x-axis and y-axis or add a Group By attribute. You can change the axis treatment or values to display if the graph uses these items.

- Specify a different graph type. You can change the type of graph. For example, you can change a line graph to a histogram. To change the type of the graph, click the button at the top of the window and specify the required information:

- Bar
A bar graph plots the values of a selected attribute (x-axis) against frequency counts (y-Axis).

- Box
A box plot summarizes binned data of a selected attribute (X axis). It graphically depicts groups of numeric data using the quantiles of the data.

- Histogram
A histogram plots the values of a selected attribute along the x-axis against frequency counts along the y-axis.

- Line or Scatter
Line charts display continuous data over time that is set against a common scale. Scatter charts are commonly used for displaying and comparing numeric values, such as scientific, statistical, and engineering data.

Bar

A bar graph plots the values of a selected attribute (x-axis) against frequency counts (y-Axis).

To specify a bar graph:

1. Click Bar.

2. Specify the following information for Bar Graph settings:

- Title: This is the name of the graph. You can use the default name or you can enter a different name.

- Comment: Provide a description for the bar graph. This is an optional field.

- Specify the following Bar Graph settings:

- X-Axis: Select one attribute to be plotted along the x-axis of the graph. This attribute is handled using either Top N, or Binned (the default). To specify handling, click Settings.

- Y-Axis: Select one attribute to be plotted along the y-axis of the graph. Specify the value for frequency count as statistic. For example, Average, Min, Max Median, Count. The statistic that you choose summarizes the values in the bin.

- Group By: Click this option to select an optional Group By attribute. This attribute groups the values in the bin (stack the bar). Select an attribute for Group By. To specify how the grouping is performed, click Settings.

3. Click OK.

Related Topics

- Settings (Graph Node)

Box

A box plot summarizes binned data of a selected attribute (X axis). It graphically depicts groups of numeric data using the quantiles of the data.

The bottom and top of the box are the first and third quartiles, and the band inside the box is the second quartile (the median). In the type of box plot created by Data Miner, the whiskers show the minimum and maximum values of all the data.

To specify a box plot or box graph:

1. Click Box.

2. Enter the following details of the Box Graph:

- Title: This is the name of the Box Graph. You can use the default name or you can enter a different name.

- Comment: Enter a description of the Box Graph. This is an optional field.

- Specify the following settings for the box graph:

- X-Axis: Select an attribute to be plotted along the x-axis of the graph. This attribute is binned using either Top N, or Binned (the default). To specify handling, click Settings.

- Group By: Click Group By to select an optional group by attribute. This attribute provides values on the y-axis. To specify how the grouping is performed, click Settings. If you specify an invalid value, a message appears explaining the problem.

3. Click OK.

Related Topics

- Settings (Graph Node)

Histogram

A histogram plots the values of a selected attribute along the x-axis against frequency counts along the y-axis.

To create a histogram:

1. Click Histogram.

2. Enter the following information for the histogram:

- Title: This is the name of the histogram. You can use the default name or you can enter a different name.

- Comment: Enter a description of the histogram. This is an optional field.

- Specify the following settings for the Histogram:

- X-Axis: Select an attribute to plot along the x-axis of the graph. This attribute is handled using either Top N, or Binned (the default). To specify handling, click Settings.

- Group By: Click Group By to select an optional Group By attribute. This attribute groups values in the bin (stack the bar). To specify how Group By is performed, click Settings. By default, a histogram with a Group By attribute is displayed as a Stacked Bar Graph. Click , the Stacked Bar Graph icon, and select the Dual Y Bar Graph icon. If you specify an invalid value, then a message appears explaining the problem.

3. Click OK.

Related Topics

- Settings (Graph Node)

Line or Scatter

Line charts display continuous data over time that is set against a common scale. Scatter charts are commonly used for displaying and comparing numeric values, such as scientific, statistical, and engineering data.

To create a Line or Scatter:

1. Click Line to create a Line graph. The Line graph is the default type. To create scatter graph, click Scatter.

2. For a line graph or a scatter graph, enter the following details:

- Title: This is the title of the graph. You can use the default name or you can provide a different name.

- Comment: Type a description of the graph. This is an optional field.

- For line graph settings, specify the following information:

- X-Axis: Select an attribute for the x-axis of the graph.

- Y-Axis: Select an attribute for the y-axis of the graph.

- Group By: Click this option to select an optional Group By attribute. Use this option to create a series based on the Group By attribute values. Select an attribute for Group By. To specify how the grouping is performed, click Settings.

3. Click OK.

Graph Node Properties

Graph node properties is identified by the node name that you are viewing.In the Properties pane, you can examine and change the characteristics or properties of a node.

To view the properties of a node, click the node and click Properties. If the Properties pane is closed, then go to View and click Properties. Alternately, right-click the node and click Go to Properties.

The Properties pane of a Graph node consists of these sections:

- Cache (Graph Node)
You have the option to generate cache of the output data.

- Details
The Details section displays the node name and comments about the node.

- Data
The Data section in Properties displays information related to the data and the data source, which can be a table or view.

Related Topics

- Properties

Cache (Graph Node)

You have the option to generate cache of the output data.

After you generate sample data, the option Generate Cache of Output Data to Optimize Viewing Results is selected.

The default value is 2000 records. You can change this value.

Details

The Details section displays the node name and comments about the node.

You can change the name of the node and edit the comments in this section. The new node name and comments must meet the requirements.

Related Topics

- Node Name and Node Comments

Data

The Data section in Properties displays information related to the data and the data source, which can be a table or view.

The Data section consists of the following:

- Source Table: Displays the name of the source table or view for the Data Source node. If no source table is associated with the node, then click … to the right of Source Table. A list of tables and views that are accessible from the data mining account is displayed. You can select the table or view. You can also use this process to change the table or view.

- Data: Displays the attributes in a grid. For each attribute, the name, the alias, and the data type are displayed. You can perform the following tasks:

- Create an alias for an attributed by entering the alias in the appropriate cell.

- Filter attributes.

- Delete attributes. Select the attribute and click .

- Edit attributes. Select the attribute and click .

- Select Attributes to include in the Data Source.

- Refresh the node. Click .

Related Topics

- Filter

- Select Attributes

SQL Query Node

SQL Query node writes SQL queries to perform special data preparation. Use the SQL Query node to provide input for a model build.

You can do the following:

- Manually enter a SQL query with the least amount of constraints possible.

- Incorporate a broader array of methodologies as part of an Oracle Data Miner workflow. You can insert any SQL query as a source of data or as a transformation by using existing data.

- Run Oracle R Enterprise scripts that are registered for the database.

- Run parallel processes or parallel queries.

- Input for SQL Query Node
The input for a SQL Query node can be data provider nodes and model provider nodes.

- SQL Query Restriction
SQL is limited to a query that returns a flow of data.

- Create a SQL Query Node
You create a SQL Query node to write a SQL query.

- SQL Query Node Editor
The SQL Query Node Editor enables you to define and validate a SQL query.

- Oracle R Enterprise Script Support
Oracle R Enterprise, a component of the Oracle Advanced Analytics option, makes the open source R statistical programming language and environment ready for the enterprise and big data.

- SQL Query Node Context Menu
The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

- SQL Query Node Properties
In the Properties pane, you can examine and change the characteristics or properties of a node.

Input for SQL Query Node

The input for a SQL Query node can be data provider nodes and model provider nodes.

A SQL Query node requires the following input:

- Zero to many data provider nodes, such as Data Source nodes and Transform nodes.

- Zero to many input model provider nodes, such as Model Build nodes and Model nodes.

Data provider nodes are used as follows:

- If there are no data provider nodes, then you define an originating Data Source node using an SQL SELECT statement that is not constrained by any input sources that must be defined within Oracle Data Miner. The statement can contain its own internal table references, such as:

Select * from a, b where a.id = b.id

When there are no data provider nodes, the source tables or views are hidden from Data Miner. Code generation cannot parameterize such tables in the generated SQL script.

- If there are one or more data provider nodes, then you can reference each data flow within the expression builder interface. You can continue to expose all data sources within the Oracle Data Miner workflow.

When a model provider node is connected as input, it enables you to see the list of model names contained in the node. This is useful in creating SQL that requires a model name.

Oracle Data Miner includes snippets that help you write SQL queries.

Related Topics

- Snippets in Oracle Data Miner

SQL Query Restriction

SQL is limited to a query that returns a flow of data.

A SQL Query node can provide data to any node that requires data, such as a node that builds a model.

Create a SQL Query Node

You create a SQL Query node to write a SQL query.

To create a SQL Query node:

1. In the Components pane, go to Workflow Editor. If the Components pane is not visible, then in the SQL Developer menu bar, go to View and click Components. Alternately, press Ctrl+Shift+P to dock the Components pane.

2. Create zero or more data provider nodes, such as Data Source nodes or Transform nodes.

3. Create zero or more model provider nodes, such as Model Build nodes or Model nodes.

4. In the Workflow Editor, expand Data, and click Create SQL Query.

5. Drag and drop the node from the Components pane to the Workflow pane.
The node is added to the workflow. The GUI shows that the node has no data associated with it. Therefore, it cannot be run.

6. Right-click the data provider and or model provider nodes. For each node, select Connect from the context menu.

7. Draw a line to the SQL Query node and click again. Ensure that you connect all the required nodes.

8. Open SQL Query Node Editor by double-clicking the SQL Query node or by selecting Edit from the context node.

9. Write the SQL query, and validate or preview it.

10. Click OK.

Related Topics

- SQL Query Node Editor

SQL Query Node Editor

The SQL Query Node Editor enables you to define and validate a SQL query.

The editor provides specifications that can be dragged and dropped or double-clicked into the query build text area. The tabs on the left of the window provide help in writing a query:

- Source is a list of input nodes that includes both Data Provider nodes and Model Provider nodes. When you select an item in the Source list, information appears in the Message box.

- Snippets are standard SQL Developer Snippets (SQL code fragments) arranged by the type of calculation.
The snippet category Predictive Query helps write queries using the DBMS_PREDICTIVE_ANALYTICS PL/SQL package.

For information about snippets, see Oracle SQL Developer User's Guide.

For a brief description of snippet functionality, move the cursor over the name. The information is displayed in a tool tip.

- PL/SQL Functions is a list of PL/SQL functions by user schema.

- R Script is a list of registered R Scripts; this tab is displayed if Oracle R Enterprise is installed.

Write SQL in the text area.

Below the text area are:

- Columns—List the columns and data types.

- Preview—Displays a small number of the rows returned by the query.

When you click OK or Validate, the following are verified:

- The query generates at least one column of output.

- All data types are data types that Oracle Data Miner supports. Most scalar data types are supported. The most common data types that are not supported are user custom object types.
If unsupported data types are found, then a table is created and an error message is displayed above the column panel. All unacceptable data types in the column list are marked with an invalid icon next to the column name.

- If no validation errors occur during the parsing of the query, then the Columns tab shows data types of the columns and the Data tab shows a small sample of results.
If a validation error occurs, then the validation panel is displayed for the Column and Data tabs.

Related Topics

- Oracle SQL Developer User's Guide

Oracle R Enterprise Script Support

Oracle R Enterprise, a component of the Oracle Advanced Analytics option, makes the open source R statistical programming language and environment ready for the enterprise and big data.

Oracle R Enterprise integrates R with Oracle Database. It is designed for problems that involve large amounts of data.

R users can develop, refine, and deploy R scripts that leverage the parallelism and scalability of the database to perform predictive analytics and data analysis.

The SQL Query node in Oracle Data Miner provides a simplified interface for integrating R scripts that have been registered with the database. This enables R developers to provide useful scripts for analyzing data.

You can run embedded R scripts that use theses interfaces:

- rqEval

- rqTableEval

- rqRowEval

- rqGroupEval

- Oracle R Enterprise Database Roles
The Oracle R Enterprise database roles are added to the OMDRUSER role.

Oracle R Enterprise Database Roles

The Oracle R Enterprise database roles are added to the OMDRUSER role.

The OMDRUSER role includes both these two roles:

- RQUSER

- RQADMIN

If the RQUSER and RQAMIN roles are not available in the database configuration at the time of Oracle Data Miner repository installation, then the roles must be added by the DBA manually to the ODMRUSER role after Oracle R Enterprise is installed.

You must register R scripts before you can use them. You can register scripts using SQL*Plus or SQL Worksheet, using connection with DBA privileges.

Registered R Scripts are listed on the R Scripts tab of the SQL Query node. There are also R code snippets, which are code fragments that help you write scripts. Some snippets are just syntax, and others are examples. You can insert and edit snippets when you are using SQL Worksheet or creating or editing R code using SQL Query node.

SQL Query Node Context Menu

The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

To see the context menu, right-click a Data Source node. The following options are available in the context menu:

- Connect

- Edit. Opens the SQL Query Node Editor.

- Validate Parents

- Run

- Force Run

- Save SQL

- Deploy

- Show Graph

- Generate Apply Chain

- Refresh Input Data Definition

- Cut

- Cut

- Extended Paste

- Paste

- Select All

- Performance Settings. This opens the Edit Selected Node Settings dialog box, where you can set Parallel Settings and In-Memory settings for the node.

- Show Event Log

- Show Runtime Errors. Displayed only if there is an error.

- Show Validation Errors. Displayed if there are validation errors.

- Go to Properties

- Navigate

SQL Query Node Properties

In the Properties pane, you can examine and change the characteristics or properties of a node.

To view the properties of a node, click the node and click Properties. If the Properties pane is closed, then go to View and click Properties. Alternately, right-click the node and click Go to Properties.

SQL Query node Properties contains these sections:

- SQL: Shows the SQL query. Click to edit the query.

- Output: Shows the columns and data types in the output of the query.

- Cache

- Details

Update Table Node

An Update Table node updates an existing table with selected columns from the data input to the node. Input columns are mapped to the existing table columns.

Update Table node can run in parallel. Update Table nodes rely on database resources for their definitions. It may be necessary to refresh a node definition if the database resources change. For example, if the resources are deleted or re-created.

- Input and Output for Update Table Node
The input for an Update Table node is any node that produces a data flow. You can connect only one node to an Update Table node.

- Data Types for Update Table Node
Update Table node supports certain data types. It can also support other types, such as B, and support these additional types with manual mapping.

- Create Update Table Node
You create an Update Table node to update an existing table with data from selected columns in the table. You can connect an Update Table node to any node that creates a data flow, such as an Apply node.

- Edit Update Table Node
The Edit Update Table dialog box provides the specification for the Update Table node.

- Update Table Node Data Viewer
After the node runs successfully, you can view the data in the Update Table Node Data Viewer.

- Update Table Node Context Menu
The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

- Update Table Node Properties
In the Properties pane, you can examine and change the characteristics or properties of a node.

Related Topics

- Refresh Nodes

- About Parallel Processing

Input and Output for Update Table Node

The input for an Update Table node is any node that produces a data flow. You can connect only one node to an Update Table node.

The output of an Update Table node is a data flow. You can use the data flow as a data source. To save the output as a table or view, use a Create Table or View node.

Related Topics

- Create Table or View Node

Data Types for Update Table Node

Update Table node supports certain data types. It can also support other types, such as B, and support these additional types with manual mapping.

You can manually map columns that do not have exact data type matches but the column data types have reasonable, safe implicit conversion default. For example, you can map BINARY_DOUBLE to NUMBER, or NVARCHAR2 to VARCHAR2. There could be some loss of data for such mappings:

- Mapping BINARY_DOUBLE or BINARY_FLOAT to NUMBER could result in the loss of precision

- Mapping NVARCHAR2 and NCHAR to VARCHAR2 can result in the loss of data because NVARCHAR2 and NCHAR are based on a potentially different character set than VARCHAR2. For mappings to work, your database must be set up so that NVARCHAR2 and NCHAR are based on the same character set as VARCHAR2.

Related Topics

- Supported Data Types for Data Source Nodes

Create Update Table Node

You create an Update Table node to update an existing table with data from selected columns in the table. You can connect an Update Table node to any node that creates a data flow, such as an Apply node.

To create an Update Table node:

1. In the Components pane, go to Workflow Editor. If the Components pane is not visible, then in the SQL Developer menu bar, go to View and click Components. Alternately, press Ctrl+Shift+P to dock the Components pane.

2. In Workflow Editor, expand Data, and click Update Table.

3. Drag and drop the node from the Components pane to the Workflow pane.
The node is added to the workflow. The GUI shows that the node has no data associated with it. Therefore, it cannot be run.

4. Move the mouse to the node in the workflow that produces the data flow to update. Right-click the node, and select Connect from the context menu.

5. Draw a line to the Update Table node and click again.

6. The Edit Update Table Node dialog box opens. You can define the characteristics of the Update Table node.

7. You can do either of the following:

- Accept the default settings for the Table Update node.

- Edit the default settings in Edit Update Table node.

8. To update the table, right-click the Update Table node, and select Run.

9. After running of the Update Table node is complete, you can view the results. Right-click the node and select View Data.

- Update Table Node Automatic Behavior
The automatic behavior of Update Table node depends on the selection of the Auto Input Column Selection option.

Related Topics

- Update Table Node Automatic Behavior

Update Table Node Automatic Behavior

The automatic behavior of Update Table node depends on the selection of the Auto Input Column Selection option.

If the Auto Input Column Selection option is selected, then the behavior under the following scenarios are:

- When input is connected—If the Update Table node has a selected table to update, then any column that matches the existing columns in the table are mapped to that column automatically. The node becomes valid, if at least one column was included in the specification.

- When input is disconnected—All columns are automatically removed. The node becomes invalid.

- When the input node is edited:

- If a column is added to the input node, then the column is added to the Update node, if there is a matching column in the existing table.

- If a column is removed, then the column is removed from the node.

- If a column is edited, then there are two possibilities:

- If the edited column no longer matches an existing column, then it is removed.

- If the edited column matches an existing column, then it is added.

If Auto Input Column Selection is not selected, then you must manually add and remove column specifications from the Update Table node.

Edit Update Table Node

The Edit Update Table dialog box provides the specification for the Update Table node.

To edit the Update Table node:

- Name: Displays the name of the table. You can select an existing table by clicking Browse Table and select an existing table. Or you can create a new table by clicking New.

- Auto Input Columns Selection: This option is selected by default. To select other columns, deselect this option. If you deselect this option, then click to select an input column. Use the arrows to move attributes from Available Attributes to Selected Attributes.

- Drop Existing Rows: If this option is selected, then existing rows in the table are dropped before the table is updated. By default, the option is not selected.

- Columns are listed in the Data grid.

- Create Table (Update Table)
All of the attributes of the attached table are listed.

- Edit Columns (Update Table)
The Edit Columns tab displays the columns of the table to be updated. For each column, the Input Name, Target Name and Target (data) type are displayed.

Related Topics

- Update Table Node Automatic Behavior

Create Table (Update Table)

All of the attributes of the attached table are listed.

You can either accept the name for the new table or select a different name.

- To delete an attribute, click .

- To edit an attribute, click .

The Select Attributes dialog box opens.

- Select Attributes (Update Table)
All columns are selected by default.

Select Attributes (Update Table)

All columns are selected by default.

You have the option to select particular attributes. If you do not want to include a column in the data:

1. Move the attribute from Selected Attributes to Available Attributes.

2. Click OK.

Edit Columns (Update Table)

The Edit Columns tab displays the columns of the table to be updated. For each column, the Input Name, Target Name and Target (data) type are displayed.

By default, the option Auto Input Columns Selection is selected. If you deselect Auto Input Columns Selection, then you must manually select input columns by clicking .

Use the arrows to move the attributes from Available Attributes to Selected Attributes.

Related Topics

- Update Table Node Automatic Behavior

Update Table Node Data Viewer

After the node runs successfully, you can view the data in the Update Table Node Data Viewer.

To view the data, right-click the node and select View Data. The data viewer is the same as Data Source Node Viewer.

Related Topics

- Data Source Node Viewer

Update Table Node Context Menu

The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

To see the context menu, right-click a Data Source node. The following options are available in the context menu:

- Connect

- Edit. Opens the Edit Update Table Node dialog box.

- Validate Parents

- Run

- View Data. Opens the Update Table Node Data Viewer dialog box.

- Show Graph

- Generate Apply Chain

- Force Run

- Cut

- Copy

- Paste

- Extended Paste

- Select All

- Performance Settings. This opens the Edit Selected Node Settings dialog box, where you can set Parallel Settings and In-Memory settings for the node.

- Show Event Log

- Show Runtime Errors. Displayed only if there is an error.

- Show Validation Errors. Displayed only if there are validation errors.

- Go to Properties

- Navigate

Related Topics

- Update Table Node Data Viewer

- Edit Update Table Node

Update Table Node Properties

In the Properties pane, you can examine and change the characteristics or properties of a node.

To view the properties of a node, click the node and click Properties. If the Properties pane is closed, then go to View and click Properties. Alternately, right-click the node and click Go to Properties.

Update Table Properties consists of these sections:

- Table (Update Table)
The Table tab displays the name of the table to update.

- Columns (Update Table)
The Columns tab displays the columns of the table that are to be updated. For each column, the Input Name, Target Name and Target (data) type are displayed.

- Cache
The Cache section provides the option to generate a cache for output data. You can change this default using the transform preference.

- Details
The Details section displays the name of the node and any comments about it.

Table (Update Table)

The Table tab displays the name of the table to update.

The default is to not drop existing rows. To drop existing rows, select Drop Existing Rows.

Columns (Update Table)

The Columns tab displays the columns of the table that are to be updated. For each column, the Input Name, Target Name and Target (data) type are displayed.

By default, the Auto Input Column Selection is selected.

If you deselect the Auto Input Column Selection then you must manually select input columns by clicking . Use the arrows to move the attributes from Available Attributes list to Selected Attributes list.

If data on the server changes, then it may be necessary to refresh the nodes. To refresh, click .

Related Topics

- Refresh Nodes

- Update Table Node Automatic Behavior

Cache

The Cache section provides the option to generate a cache for output data. You can change this default using the transform preference.

You can perform the following tasks:

- Generate Cache of Output Data to Optimize Viewing of Results: Select this option to generate a cache. The default setting is to not generate a cache.

- Sampling Size: You can select caching or override the default settings. Default sampling size is Number of Rows Default Value=2000

Related Topics

- Transforms

Details

The Details section displays the name of the node and any comments about it.

You can change the name and comments in the fields here:

- Node Name

- Node Comments

Target Values Selection

The Target Values Selection dialog box displays the number of target values selected. The default setting is Automatic.

It uses the top 10 Target Class Values by frequency. You can change the number of target values by changing Frequency Count. You can also select Use Lowest Occurring.

To select custom values:

1. Select Custom.

2. Move the values from Available Values to Selected Values.

3. After you are done, click OK.

6 Using the Oracle Data Miner GUI

The Oracle Data Miner graphical user interface (GUI) is based on the GUI for SQL Developer 4.0.

In SQL Developer, click Help and then click SQL Developer Concepts and Usage for an overview of the SQL Developer GUI. The following topics describe the common procedures and menus for the GUI that are specific to data mining and the Oracle Data Miner:

- Graphical User Interface Overview
The Oracle Data Miner window generally uses the left side for navigation to find and select objects and the right side to display information about selected objects.

- Oracle Data Miner Functionality in the Menu Bar
The menus at the top of the Oracle SQL Developer window contain standard entries, and entries for features specific to Oracle Data Miner.

- Workflow Jobs
Workflow Jobs displays all running and recently run tasks, arranged according to connection.

- Projects
Projects reside in a connection. Projects contain all the workflows created in the data mining process.

- Miscellaneous
This section describes common controls and tasks that you can perform using the Oracle Data Miner GUI.

Related Topics

- Oracle SQL Developer User's Guide

Graphical User Interface Overview

The Oracle Data Miner window generally uses the left side for navigation to find and select objects and the right side to display information about selected objects.

Note:

This text explains the default interface. However, you can customize many aspects of the appearance and behavior of Oracle Data Miner by setting the Oracle Data Miner preferences.

Here is a simple workflow:

- About the Data Miner Tab: Positioned in the left pane. You manage database connections here.

- Workflow Jobs: Positioned in the left pane. You can view the status for running tasks.

- Components: Positioned in the right pane. You select the nodes of the workflow here.

- Menu bar: Positioned at the top of the window.

- Workflows are displayed in the middle pane of the window.

- Properties: You can put this pane any place you want. It is useful to position the Properties pane just below the workflow that you are viewing.

Some settings are sticky settings. If you change a sticky setting, the, the new value becomes the default value.

Related Topics

- Data Miner Preferences

- Oracle Data Miner Functionality in the Menu Bar

Oracle Data Miner Functionality in the Menu Bar

The menus at the top of the Oracle SQL Developer window contain standard entries, and entries for features specific to Oracle Data Miner.

You can use keyboard shortcuts to access menus and menu items. For example Alt+F for the File menu and Alt+E for the Edit menu, or Alt+H, then Alt+S for Full Text Search of the Help topics. You can also display the File menu by pressing the F10 key.

The icons just below the menus perform a variety of actions, including the following:

- New: Opens the New Gallery to define new database objects, such as a new database connection.

- Open: Opens a file.

- Save: Saves any changes to the currently selected object.

- Save All: Saves any changes to all open objects.

- Undo: Reverses the last operation.
There are several forms of Undo, including Undo Create to undo the most recent create node, Undo Edit Node to undo the latest edit node, and so on.

- Redo: Does the latest undo operation again.
There are several forms of Redo, including Redo Create to redo the most recent create node, Redo Edit Node to redo the latest edit node, and so forth.

- Back: Moves to the pane that you most recently visited. Use the drop-down arrow to specify the tab view.

- Forward: Moves to the pane after the current one in the list of visited panes. Or use the drop-down arrow to specify a tab view.

These menus contain functionality specific to Oracle Data Miner:

- View Menu
From the View menu, you can access the options related to Data Miner tab, workflow jobs, scheduled workflow jobs, and workflow structure.

- Tools Menu
From the Tools menu, you can access the options related to Data Miner and Data Miner preferences.

- Diagram Menu
The Diagram menu is available when a workflow is open.

- Oracle Data Miner Online Help
The online help specific to Oracle Data Miner is in the help folder Oracle Data Miner Concepts and Usage.

View Menu

From the View menu, you can access the options related to Data Miner tab, workflow jobs, scheduled workflow jobs, and workflow structure.

The following options are available in the View menu of Oracle Data Miner:

- Click Data Miner Connections to open the Data Miner tab.

- Click Workflow Jobs to dock the Workflow Jobs pane.

- Click Scheduled Jobs to dock the Scheduled Jobs pane.

- Click Structure to view the workflow structure in the Workflow Structure pane.

- Data Miner
Using the Data Miner menu option , you can open the Data Miner tab, access the Schedule Jobs option, and drop the repository.

- Workflow Jobs
Workflow Jobs displays all running and recently run tasks, arranged according to connection.

- Scheduled Jobs
In the Scheduled Jobs window, you can view and manage scheduled workflow jobs. You can view details such as the workflow name, project name, and the date and time of the next scheduled run.

- Structure Window
The Structure window shows the structure of a workflow or certain model viewers that are currently active.

Related Topics

- About the Data Miner Tab

Data Miner

Using the Data Miner menu option , you can open the Data Miner tab, access the Schedule Jobs option, and drop the repository.

The following options are available in the Data Miner option under Tools menu:

- Make Visible: Opens the Data Miner tab and Workflow Jobs.

- Scheduled Jobs: Opens the Scheduled Jobs dialog box that displays the list of scheduled workflow jobs.

- Drop Repository: Drops the Data Miner repository.

These actions are usually performed as part of the Oracle Data Miner installation.

Workflow Jobs

Workflow Jobs displays all running and recently run tasks, arranged according to connection.

A task consists of running of a selected node and any ancestor nodes that must be run before running the node.

The following conditions apply for Workflow Job display:

- Workflow Jobs displays the most recent run of a workflow.

- When two or more tasks are active, the Workflow Jobs window is automatically displayed.

- By default, the Workflow Jobs automatically displays the connection selected in the tab. To view tasks in a different connection, select a connection from the list at the top of the tab.

- Workflow jobs specifies for how long a task is displayed.

You an perform multiple tasks from the Workflow Jobs context menu. Right-click a line in the grid or below the lines in the grid.

Related Topics

- Workflow Jobs

Scheduled Jobs

In the Scheduled Jobs window, you can view and manage scheduled workflow jobs. You can view details such as the workflow name, project name, and the date and time of the next scheduled run.

To view the latest scheduled workflow jobs, you must click the refresh icon. You can perform the following tasks:

- Cancel schedule

- Edit schedule

- Open the workflow

- View Event Log

Structure Window

The Structure window shows the structure of a workflow or certain model viewers that are currently active.

The nodes in a tree or workflow are listed in a flat list, which does not show parent or child relationships. The links are the keys that tie the nodes together.

When you view nodes and links in the Structure window, the workflow editor reacts by immediately making the selected items visible. This property is useful when you are navigating a complex workflow or tree.

- Structure Window and Workflow

- Structure Window and Model Viewers

- Structure Window Controls

Structure Window and Workflow

Suppose that you have a two-node workflow simple consisting of a Data Source node and a Classification Build node.

The view of the workflow simple in the Structure window:

If you select an item in the Structure window, then the corresponding item is selected in the workflow. For example, if you go to the Links folder, and select From "DATA_MINING_BUILD_V" to "Class Build", then the link between the two nodes in simple is highlighted.

Structure Window and Model Viewers

These model viewers include a Tree tab:

- k-Means and O-Cluster model viewer for the Clustering models

- Decision Tree model viewer for the DT Classification model

The Tree tab of the model viewer illustrates how the rules generated by the model are related. These trees are sometime large and complex. You can use the Structure window to navigate among the nodes of the tree, just as you navigate among the nodes of a workflow. In the Structure window, select a node or an item in the Link folder. The node or link in the model viewer is automatically highlighted.

Structure Window Controls

The Structure window supports these controls:

- To freeze the Structure window on the current view, click .
A window that is frozen does not track the active selection in the active window.

- To open a new instance of the Structure window, click .
The new view appears as a new tab in the Structure window.

Tools Menu

From the Tools menu, you can access the options related to Data Miner and Data Miner preferences.

The following options are available in the Tools menu of Oracle Data Miner:

- Data Miner
Using the Data Miner menu option , you can open the Data Miner tab, access the Schedule Jobs option, and drop the repository.

- Data Miner Preferences
You can set preferences for Oracle Data Miner in the Preference option in the Tools menu.

Data Miner

Using the Data Miner menu option , you can open the Data Miner tab, access the Schedule Jobs option, and drop the repository.

The following options are available in the Data Miner option under Tools menu:

- Make Visible: Opens the Data Miner tab and Workflow Jobs.

- Scheduled Jobs: Opens the Scheduled Jobs dialog box that displays the list of scheduled workflow jobs.

- Drop Repository: Drops the Data Miner repository.

These actions are usually performed as part of the Oracle Data Miner installation.

Data Miner Preferences

You can set preferences for Oracle Data Miner in the Preference option in the Tools menu.

To set preferences, click Tools and then Preferences. Under Preferences, click Data Miner. Data Miner preferences are divided into several sets:

- Node Settings
Node settings specify the behavior of workflow nodes.

- Viewers
In Viewers, you can specify settings for data viewers and model viewers.

- Workflow Editor
In the Workflow Editor, you can specify preferences related to nodes, link style, and alternate link routing.

- Workflow Import/Export
Workflow Directory is the default directory to import workflows from and to export works.

- Workflow Jobs
Preferences for the Workflow Jobs specify which connection is displayed and how long the workflow status is displayed

- Workflow Scheduler
In the Workflow Scheduler dialog box, you can set email notifications and preferences for workflow jobs and workflow schedules.

Node Settings

Node settings specify the behavior of workflow nodes.

You can specify settings related to model build, performance options, and transforms.

- Models
Model settings specify properties related to model build, model details, Apply, Text nodes, Test nodes and so on.

- Performance Options
You can edit the Parallel Processing and In-Memory settings for one or all nodes in the Preferences dialog box.

- Transforms
You can specify preferences that applies to all transformation as well as individual transformations related to Filter Columns, Filter Column Details, join transformation, and sampling.

Models

Model settings specify properties related to model build, model details, Apply, Text nodes, Test nodes and so on.

- Apply
Lists the preferences that specify how an Apply node operate.

- Model Build
The default values for model build function depends on the mining function.

- Model Details
By default, a Model Details node uses automatic settings.

- Test
By default, a Test node uses Automatic Setting.

- Text
The settings in Text describe how text is handled during a model build.

Apply

Lists the preferences that specify how an Apply node operate.

The preferences are:

- Automatic Apply Settings Default is either Automatic or Manual. By default, the settings are set to Automatic.

- Automatic Data Settings Default is either Automatic, the default, or Manual.

- Default Column Order is either Data Columns First or Apply Columns First. Default Columns First is set as the default.

Related Topics

- Automatic Settings

Model Build

The default values for model build function depends on the mining function.

Specify default values for model build options:

- Association
By default, an Association node builds one model using the Apriori algorithm.

- Classification
A Classification node automatically generates four models by default.

- Clustering
By default, a Clustering node builds two models, one each using O-Cluster and k-Means.

- Feature
By default, a Feature Extraction node builds a Nonnegative Matrix Factorization model.

- Regression
By default, a Regression node builds two models, one each using General Linear Model and Support Vector Machine.

Association

By default, an Association node builds one model using the Apriori algorithm.

The default maximum distinct count for item values is 10. Change the default to a different integer.

Classification

A Classification node automatically generates four models by default.

A Classification node uses any of these algorithms to generate the models:

- Decision Tree

- General Linear Model

- Naive Bayes

- Support Vector Machine

All four models have the same input data, the same target, and the same case ID (if a case ID is specified).

If you do not want to build models using one of the default algorithms, then deselect that algorithm. You can still add models using the deselected algorithm to a Classification node.

By default, the node generates these test results for tuning:

- Performance Metrics

- Performance Matrix (Confusion Matrix)

- ROC Curve (Binary only)

- Lift and Profit. The default is set to the top 5 target values by frequency. You can edit the default setting. By default, the node does not generate selected metrics for Model tuning. You can select the metrics for Model tuning.

You can deselect any of the test results. For example, if you deselect Performance Matrix, then a Performance Matrix is not generated by default.

By default, split data is used for test data. Forty percent of the data is used for testing, and the split data is created as a table. You can change the percentage used for testing and you can create the split data as a view instead of a table. If you create a table, then you can create it in parallel. You can use all of the build data for testing, or you can use a separate test source.

Related Topics

- Testing Classification Models

- Tuning Classification Models

Clustering

By default, a Clustering node builds two models, one each using O-Cluster and k-Means.

If you are connected to Oracle Database 12c Release 1 (12.1) or later, then a Clustering node also builds an Expectation Maximization model, so that three models are built.

All Clustering models in the node have the same input data and the same Case ID, if one is specified.

If you do not want to build models using one of the algorithms by default, then deselect that algorithm. A user will still be able to add models using the deselected algorithm to a Clustering node.

Feature

By default, a Feature Extraction node builds a Nonnegative Matrix Factorization model.

If you are connected to Oracle Database 12c Release 1 (12.1) or later, then the node also builds a Principal Component Analysis model. You can specify that the node builds a Singular Value Decomposition model.

If you do not want to build models using one of the default algorithms, then deselect that algorithm. You can still add models using the deselected algorithm to a Classification node.

Regression

By default, a Regression node builds two models, one each using General Linear Model and Support Vector Machine.

All models in the node have the same input data, target, and case ID, if a case ID is specified.

If you do not want to build models using one of the default algorithms, then deselect that algorithm. You can still add models using the deselected algorithm to a Regression node.

By default, two test results, Performance Metrics and Residuals, are calculated.

By default, split data is used for the test data. The split is 40 percent and the split data is created as a view.

Model Details

By default, a Model Details node uses automatic settings.

You can deselect automatic settings for a specific model details node, or you can deselect automatic settings for all model details nodes.

Test

By default, a Test node uses Automatic Setting.

You can:

- Deselect Automatic Settings for a particular Test node.

- Deselect Automatic Settings for all Test nodes.

Text

The settings in Text describe how text is handled during a model build.

The default Categorical Cutoff Value is 200

The Default Transformation Type is Token.

Token Transformation Settings use these defaults:

- Language: English (default)

- Stemming (Deselected)

- Stoplist: Default Stoplist

- Maximum Number across all documents: 3000

Theme Transformations Settings use these defaults:

- Language: English (default)

- Stoplist: Default Stoplist

- Maximum Number of themes across all Documents: 3000

Performance Options

You can edit the Parallel Processing and In-Memory settings for one or all nodes in the Preferences dialog box.

The Parallel Processing settings and In-Memory settings for all nodes are displayed in the Preferences dialog box, which opens when you click Data Miner under Preferences in the Tools menu.

- Click Parallel Settings and select any one of the following options:

- Enable: Select one or more nodes and click Enable to enable parallel processing for the selected nodes.

- Disable: Select one or more nodes and click Disable to remove the parallel processing setting from the selected nodes.

- All: To set parallel processing for all nodes.

- None: To remove parallel processing settings from all nodes.

- Click In-Memory Settings and select any one of the following options:

- Enable: Select one or more nodes and click Enable to enable In-Memory settings for the selected nodes.

- Disable: Select one or more nodes and click Disable to remove the In-Memory settings from the selected nodes.

- All: To set In-Memory settings for all nodes.

- None: To remove In-Memory settings from all nodes.

- Click to specify parallel processing and In-Memory settings for a selected node. This opens the Edit Node Performance Settings dialog box.

Related Topics

- About Parallel Processing

- Edit Node Performance Settings

Transforms

You can specify preferences that applies to all transformation as well as individual transformations related to Filter Columns, Filter Column Details, join transformation, and sampling.

The preference that applies to all transformations is:

- Generate Cache Sample Table to Optimize Viewing: The default setting is to not generate a cache sample table. Generating a sample cache table is useful if you are processing large amounts of data.

For individual transformations, the options are:

- Filter Columns
The preferences specify the behavior of the Filter Columns Node transformation.

- Filter Columns Details
The preferences specify the behavior of Filter Columns Details node transformation.

- Join
The preferences specify the behavior of Join node transformation.

- Sampling
The preferences specify the behavior of sampling.

Filter Columns

The preferences specify the behavior of the Filter Columns Node transformation.

You can specify the following Data Quality criteria:

- % Nulls less than or equal: Indicates the largest acceptable percentage of NULL values in a column of the data source. The default value is 95%.

- % Unique less than or equal: Indicates the largest acceptable percentage of values that are unique to a column of the data source. The default value is 95%.

- % Constant less than or equal: Indicates the largest acceptable percentage of constant values in a column of the data source.

You can specify the following Attribute Importance settings:

- Importance Cutoff: A number between 0 and 1.0. The default cutoff is 0.

- Top N: The maximum number of attributes. The default is 100.

- Attribute Dependency: Select this option to generate pairwise dependency information. In case of supervised mode, you can modify the output columns that are used in the result. Attribute Dependency is selected by default if Attribute Importance is selected.

Note:
You must select Attribute Importance to generate Attribute Dependency.

Sampling (Data Quality and Attribute Importance): Enables Filter Column settings according to the default size for random sample for calculating statistics. The default values for sampling are specified in preferences. You can change the default or even turn off sampling. The default sample size is 10,000 records.

The Column Filter node, by default, uses sampling to determine Data Quality and Attribute Importance. The default is to use a sample size of 2000 records. You can turn off sampling, that is use all of the data, or increase the sample size.

Filter Columns Details

The preferences specify the behavior of Filter Columns Details node transformation.

By default, Automatic Settings is selected.

Related Topics

- Filter Columns Node

- Filter Columns Details

Join

The preferences specify the behavior of Join node transformation.

The settings are:

- Automatic Key Column Deletion:

- Automatic (default)

- Manual

- Default

- Automatic Data Column Default:

- Automatic (default)

- Manual

- Default

Sampling

The preferences specify the behavior of sampling.

There are three preferences for sampling:

- Sampling Type: By default, the Sampling Type is Random with the Seed
Sampling Type can be changed to TopN.

- Seed: By default, it is set to 12,345. You can change the value of Seed.

- Sampling Size: This is either the number of rows or the percentage for the sampling size. The default size is either 2000 rows or 60%.

Viewers

In Viewers, you can specify settings for data viewers and model viewers.

Viewers settings specify the behavior of:

- Explore Data Viewer

- Graphical Settings

- Models

- Association Rules

- Cluster Tree

- Decision Tree

- Data
In the Data section, you can specify settings for Explore Data viewer and graphical settings.

- Model
In the Models section, you can specify preferences that apply to all Model Viewers.

Data

In the Data section, you can specify settings for Explore Data viewer and graphical settings.

The options are:

- Explore Data Viewer
You can specify precision settings for the Explore Data viewer here. These preferences specify data precision for data viewed in the Explore Data node.

- Graphical Settings
In the Graphical Settings section, you can specify preferences for charts and graphs.

Explore Data Viewer

You can specify precision settings for the Explore Data viewer here. These preferences specify data precision for data viewed in the Explore Data node.

Precision is the maximum number of significant decimal digits, where the most significant digit is the left-most nonzero digit, and the least significant digit is the right-most known digit. Precision is an integer greater than or equal to zero.

The default precision for both Percentage Based Values and Numeric Values is 4. You can change either or both of these values.

Graphical Settings

In the Graphical Settings section, you can specify preferences for charts and graphs.

Set preferences for:

- Depth Radius: The default Depth Radius is 0.

- Chart Style: The default Chart Style is Nautical.

Model

In the Models section, you can specify preferences that apply to all Model Viewers.

These general preferences apply to all Model viewers.

- Precision Level Settings: Specify precision:

- For Percentage Based Values, the default precision is 4.

- For Numerical Values, the precision is 8.

- Fetch Size Settings: Specify the number of items fetched. The default fetch size for:

- Association Rule Model: 1000

- Clustering Rules Model: 20

- All Other Models: 1000

There are additional preferences for the tree displays:

- Cluster Tree
You can specify the preferences for the Cluster Tree model display.

- Decision Tree
You can specify the preferences for the Decision Tree display.

Related Topics

- Explore Data Viewer

Cluster Tree

You can specify the preferences for the Cluster Tree model display.

Cluster Tree display contains:

- Default Node Display: A detailed header

- Default Layout: Vertical

There are also settings for Cluster Tree nodes.

- Tree Node
You can specify the preferences for each Tree node in a Cluster Tree.

Tree Node

You can specify the preferences for each Tree node in a Cluster Tree.

For each Tree Node in a cluster tree:

- The maximum display length for the Centroid Attribute Name is 25.

- The maximum display length for Centroid Value is 25.

Decision Tree

You can specify the preferences for the Decision Tree display.

Decision Tree display contains:

- Default node Display: Histogram and Detailed Header.

- Sort Target Values By: Root Node order. You can also sort by Confidence.

- Default Layout: Vertical. You can also choose Horizontal.

There are also settings for Decision Tree .

- Tree Node
You can specify the preferences for each Tree node in a Decision Tree.

Tree Node

You can specify the preferences for each Tree node in a Decision Tree.

For each Tree Node in a Decision tree, the Maximum Length of the target value is 25 characters. You can change this value.

Workflow Editor

In the Workflow Editor, you can specify preferences related to nodes, link style, and alternate link routing.

The preferences and their default settings are:

- Node Assist: Selected by default. Wizards are automatically displayed when a node is created or connected. For example, if you add a Data Source node to a workflow, then the Data Source Editor is automatically opened. You can deselect this option.

- Link Style:

- Direct: (Default) In the Direct link style, links are straight lines from one node to another with short segments. Direct link style produces a more compact, direct diagram layout.

- Orthogonal: In the Orthogonal link style, links between the nodes are at 90 degrees.

- Alternate Link Routing: Deselected by default.

Workflow Import/Export

Workflow Directory is the default directory to import workflows from and to export works.

The default value for Workflow Directory is the default for the operating system where Oracle Data Miner is installed. For example, Workflow Directory is My Documents for Microsoft Windows operating systems.

To change the Workflow Directory, enter the name of the new directory or click Browse to browse for the directory. After you specify the directory name, click OK.

Workflow Jobs

Preferences for the Workflow Jobs specify which connection is displayed and how long the workflow status is displayed

The settings are:

- Automatically Display Connection Selected in Navigator: By default, this option is selected. You can deselect it. If you deselect this option, then you must explicitly select a connection.

- Don't Display Jobs Older Than 24 Hours: By default, this option is selected. You can change this option.

Workflow Scheduler

In the Workflow Scheduler dialog box, you can set email notifications and preferences for workflow jobs and workflow schedules.

Note:

To receive email notifications, you must have an email server set up properly.

- In the Notification tab:

1. Select Enable Email Notification to receive notifications.

2. In the Recipients field, enter the email addresses to receive notifications.

3. In the Subject field, enter an appropriate subject.

4. In the Comments fields, enter comments, if any.

5. Select one or more events for which you want to receive the notifications:

- Started: To receive notifications for all jobs that started.

- Succeeded: To receive notifications for all jobs that succeeded.

- Failed: To receive notifications for all jobs that failed.

- Stopped: To receive notifications for all jobs that stopped.

6. Click OK.

- In the Settings tab:

1. In the Time Zone field, select a time zone of your preference.

2. In the Job Priority field, set the priority of the workflow job by placing the pointer between High and Low.

3. Select Max Failure and set a number as the maximum number of failed workflow execution.

4. Select Max Run Duration and set the days, hours and minutes for the duration of maximum run time of the workflow job.

5. Select Schedule Limit and set the days, hours and minutes.

6. Click OK.

Diagram Menu

The Diagram menu is available when a workflow is open.

Use the options on this menu to arrange workflow nodes. The Diagram menu has these options:

- Connect
Use the Connect option to connect two nodes in a workflow.

- Align
Use the Align option to align a set of elements and normalize the size of elements.

- Distribute
Use the Distribute option to evenly distribute (horizontally and vertically) selected elements in a diagram.

- Bring to Front
Use the Bring to Front option to move the selected node in front of all other nodes.

- Send to Back
Use the Send to Back option to move the selected node behind all other nodes.

- Zoom
Use the Zoom option for a magnified view.

- Publish Diagram
Use the Publish Diagram option to save the current workflow as a graphic file in your system.

Connect

Use the Connect option to connect two nodes in a workflow.

- To connect a node: Select Diagram and click Connect. Link the selected node to another node by drawing a line from the selected node. You can only make valid selections.

- To cancel a connection: To cancel a line that is not connected to anything, press Esc. This is the same operation as using the Connect option from the node context menu.

Align

Use the Align option to align a set of elements and normalize the size of elements.

The settings are:

Horizontal Alignment

- None: (Default) Performs no horizontal alignment.

- Top: Aligns the top edges of the selected elements.

- Middle: Aligns the horizontal centers of the selected elements.

- Bottom: Aligns the bottom edges of the selected elements.

Vertical Alignment

- None: (Default) Performs no vertical alignment.

- Left: Aligns the left edges of the selected elements.

- Middle: Aligns the vertical centers of the selected elements.

- Right: Aligns right edges of the selected elements.

Size Adjustments

- Same Width: Changes the width of the selected elements to the average width of all the selected elements.

- Same Height: Changes the height of the selected elements to the average height of all the selected elements.

Distribute

Use the Distribute option to evenly distribute (horizontally and vertically) selected elements in a diagram.

The settings are:

- Horizontal Distribution: Changes the left or right distribution of the selected diagram elements.

- Vertical Distribution: Changes the up or down distribution of the selected diagram objects.

Bring to Front

Use the Bring to Front option to move the selected node in front of all other nodes.

Send to Back

Use the Send to Back option to move the selected node behind all other nodes.

Zoom

Use the Zoom option for a magnified view.

To zoom the view, go to Diagram and click Zoom. This menu is displayed:

The default zoom setting is 100%. To return to the default, select 100%.

- To zoom in or zoom out of an entire workflow: Click and respectively, or set a specific percentage.

- To zoom in on a specific node or nodes: Select the node and then go to Diagram and click Zoom.

- To fit the entire workflow in the window: Select Fit to Window.

Publish Diagram

Use the Publish Diagram option to save the current workflow as a graphic file in your system.

To publish a diagram:

1. Open the workflow that you want to save as a graphic file.

2. Go to Diagram and select Publish.

3. Browse for a location to save the graphic file.

4. Select the graphic file format. The supported file formats are:

- PNG (default)

- SVG

- SVGZ

- JPEG

5. Click OK. The workflow is saved as a graphic file.

Oracle Data Miner Online Help

The online help specific to Oracle Data Miner is in the help folder Oracle Data Miner Concepts and Usage.

To view or search the online help for Oracle Data Miner click Help and then click Table of Content. Then expand the Table of Content and go to Oracle Data Miner Concepts and Usage on the Contents tab of Help Center.

To get help for a specific dialog box, click the Help button or press F1. To get help for objects in a workflow, select the object and press the F1 key.

Online help contains reference topics and the topics that describe how the GUI works. To see reference topics, either expand the help contents in the online help or search in the online help.

Workflow Jobs

Workflow Jobs displays all running and recently run tasks, arranged according to connection.

A task consists of running of a selected node and any ancestor nodes that must be run before running the node.

The following conditions apply for Workflow Job display:

- Workflow Jobs displays the most recent run of a workflow.

- When two or more tasks are active, the Workflow Jobs window is automatically displayed.

- By default, the Workflow Jobs automatically displays the connection selected in the tab. To view tasks in a different connection, select a connection from the list at the top of the tab.

- Workflow jobs specifies for how long a task is displayed.

You an perform multiple tasks from the Workflow Jobs context menu. Right-click a line in the grid or below the lines in the grid.

- Viewing Workflow Jobs
The Workflow Jobs viewer displays the workflows and other details such as the project, workflow status, connections.

- Working with Workflow Jobs
Lists the tasks that you can perform with Workflow Jobs.

- Workflow Jobs Grid
The Workflow Jobs grid displays the workflow name, project name, and workflow status.

- View an Event Log
In the Event Log, you can view the log of data mining events.

- Workflow Jobs Context Menu
To view the context menu, right-click a line in the Workflow Jobs grid or in a blank area of the grid.

Related Topics

- Workflow Jobs

Viewing Workflow Jobs

The Workflow Jobs viewer displays the workflows and other details such as the project, workflow status, connections.

To view Workflow Jobs:

1. In the SQL Developer menu bar, go to View and click Data Miner. If the Data Miner tab is not visible, then go to Tools and click Data Miner. Under Data Miner, click Make Visible.

2. Under Data Miner, click Workflow Jobs.

Working with Workflow Jobs

Lists the tasks that you can perform with Workflow Jobs.

You can perform the following tasks:

- View a particular task: Select the connection in which the task is running. Connections are listed in a drop-down list just above the Workflow Jobs grid.

- View and Event Log

- Terminate a running workflow: Click .

- View a log: Right-click a process in the Workflow Jobs and click View Log.

Related Topics

- View an Event Log

- Workflow Jobs Grid

Workflow Jobs Grid

The Workflow Jobs grid displays the workflow name, project name, and workflow status.

You can also edit the Workflow Jobs preferences here, by clicking the gear icon. The Workflow Jobs grid displays the following:

- Workflow name

- Project name

- Status. For Status, the values are:

- ACTIVE: Indicates that the workflow has been executed. Indicated by .

- INACTIVE: Indicates that the workflow is idle.

- STOPPED: Indicates that the workflow has been stopped.

- SCHEDULED: Indicates that the workflow is scheduled to run.

- FAILED: Indicates that the workflow execution has failed. Indicated by .

View an Event Log

In the Event Log, you can view the log of data mining events.

To view a log of data mining events in the selected connection, right-click an entry in the workflow jobs and select Show Event Log. You can also click at the top of a workflow, just under the tab.

By default, all errors are displayed. You can display errors or informational events. The total number of events and the number of events displayed is at the top of the list. For example, Events: 2 of 90 means that 2 of the 90 events are displayed.

Each error or warning has a message and details associated with it. Select an event. The message and details are displayed in the lower pane of the Event Log window.

For each data mining event in the selected connection, the following are displayed:

- Event: In Oracle Data Miner, events indicate the beginning and end of actions, such as START(WORKFLOW) and END(WORKFLOW). Each node is processed sequentially.

- Job: The name of the job that processes the event. These jobs are internal to Oracle Data Miner.

- Node: The name of the node that is being processed. Not all events are associated with a node.

- Sub-node: An internal step during node processing. For example, an Anomaly Build node has a sub-node that builds the model.

- Time: Start time of the event.

- Message: A message about the event. If the event did not encounter problems, then there is no message.
To see more information about the message, including message details, select the event. The message and the details are displayed in the pane below the list of events. Not all events have messages or message details associated with them.

- Duration: The amount of time that is spent on processing the event. The duration is displayed for END events. The duration is displayed in days, hours, minutes and seconds.

All errors are shown. You can select the type of event to display by clicking the icons above the list of events:

- To display errors, and events that failed, click .
The default is to display errors only.

- To display warnings, click .

- To display informational messages, such as the start and end of operations, click .

- To refresh, click .

- To search for events, click the down arrow next to . You can search by Node (default) or by Any to search for anything that is not a node.

- Informational Messages
Informational messages show the beginning and end of internal operations performed when a workflow runs.

Informational Messages

Informational messages show the beginning and end of internal operations performed when a workflow runs.

The following informational messages describe a successful build of an Anomaly Detection Model. The workflow took 5 minutes and 12 seconds to run. There were 26 events.

Workflow Jobs Context Menu

To view the context menu, right-click a line in the Workflow Jobs grid or in a blank area of the grid.

The context menu options are:

- Go to Workflow: Displays the workflow for this result.

- View an Event Log

- Sort by Most Recent: Sorts the entries.

- Preferences: Displays the Workflow Jobs.

Projects

Projects reside in a connection. Projects contain all the workflows created in the data mining process.

You must create at least one project in a connection.

Related Topics

- Data Miner Projects

Miscellaneous

This section describes common controls and tasks that you can perform using the Oracle Data Miner GUI.

- Filter
Use the Filter option to refine your search.

- Import Data (Oracle Data Miner)
The SQL Developer Import Data wizard allows you to import data into a database table.

- Filter Out Objects Associated with Oracle Data Mining
The Filter option filters out Oracle Data Miner (DR) objects and Oracle Data Mining (DM) objects.

- Copy Charts, Graphs, Grids, and Rules
You can export charts, graphs, grids, and also Cluster and Decision Tree Rules for use in external documents by copying them to the Microsoft Windows clipboard or saving them to a file.

Filter

Use the Filter option to refine your search.

To filter your search:

- To display only those items that you are interested in, click . In other words, you can filter out items that you are not interested in, by using this option. There are several filter options and a default option. To select a different filter option, click the triangle beside the binoculars icon.

- To clear the search, click .

Import Data (Oracle Data Miner)

The SQL Developer Import Data wizard allows you to import data into a database table.

You can create a table and import data into it, or you can import data into an existing table. In other words, you can import delimited data in an operating system file to the database.

To import data:

1. In SQL Developer, go to Connections.

2. Expand the connection that you are using for data mining.

3. Right-click the Tables node or a table name and select Import Data.

4. Specify the files from which to import the data. The file can be one of the following formats: XLS, TXT, CSV.

5. Click OK.

Filter Out Objects Associated with Oracle Data Mining

The Filter option filters out Oracle Data Miner (DR) objects and Oracle Data Mining (DM) objects.

To filter the tables associated with data mining:

1. In SQL Developer, go to Connections.

2. Expand the user account that you use for data mining and select Tables.

3. In the tool bar for Connections, click .

4. Select these filter criteria:

NAME NOT LIKE DR$%
NAME NOT KLIKE DM$%
NAME NOT LIKE ODMR$%

5. Click OK to filter the tables.

Copy Charts, Graphs, Grids, and Rules

You can export charts, graphs, grids, and also Cluster and Decision Tree Rules for use in external documents by copying them to the Microsoft Windows clipboard or saving them to a file.

You can perform the following tasks:

- Copy charts to the clipboard or save to a file: To copy charts, right-click the chart and select any one of the following options in the content-menu:

- Copy to Clipboard

- Save Image As

- View Data

- Copy data grids to the clipboard: You can copy one or multiple rows in the Graph Data dialog box. To copy data grids:

1. Select the row that you want to copy in the Graph Data dialog box, while pressing the Ctrl key. To select multiple rows, click the rows while pressing the Ctrl key and Shift keys together.

2. Then copy the selected rows by pressing the Ctrl + C keys.

3. To paste the copied rows in the clipboard, press Ctrl + V keys.

- Copy and view data content of charts: To copy data content of charts, right-click and select any one of the following options in the content menu:

- Copy Image to Clipboard

- Save Image As

- Copy Cluster and Decision Tree Rules to the clipboard or to a file: To copy Cluster and Decision Tree Rules to a clipboard or a file, use the Save Rules option in the Model viewer.

7 Transforms Nodes

A Transforms node performs one or more transformations on the table or tables identified in a Data node.

Transformations are available in the Transforms section in the Components pane. The Evaluate and Apply Data nodes must be prepared in the same way that build data was prepared.

Transform nodes includes:

- Aggregation
Aggregation is the process of consolidating multiple values into a single value.

- Data Viewer
You can view data when the Transform node is in a valid state.

- Expression Builder
Expression Builder helps you to enter and validate SQL expressions, such as constraints for filters.

- Filter Columns Node
Filter Columns filters out columns so that the columns are not used in subsequent workflow calculations.

- Filter Columns Details
The Filter Columns Details node creates a data flow that consists of the result of Attribute Importance.

- Filter Rows
A Filter Rows node enables you to select rows by specifying a SQL statement that describes the rows.

- Join
A Join node combines data from two or more Data Source nodes into a new data source.

- JSON Query
The support for the JSON data format in Oracle Data Miner (SQL Developer 4.1 and later ) is facilitated by the JSON Query node.

- Sample
You can sample your data in the Sample tab.

- Transform
A Transform node can use either sampled data or all data to calculate statistics.

Aggregation

Aggregation is the process of consolidating multiple values into a single value.

For example, you could aggregate sales in several states into sales for a region that consists of several states. To perform aggregation, use an Aggregate node.

These topics describe Aggregate nodes:

- Creating Aggregate Nodes
You must identify a Data Source node and columns to aggregate to create an Aggregation node.

- Editing Aggregate Nodes
You can define and edit aggregation elements of an Aggregate node in the Edit Aggregate Node dialog box.

- Aggregate Node Properties
In the Properties pane, you can examine and change the characteristics or properties of a node.

- Aggregate Node Context Menu
The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

Related Topics

- About Parallel Processing

Creating Aggregate Nodes

You must identify a Data Source node and columns to aggregate to create an Aggregation node.

Identify or create the node to aggregate. The node can be any node that provides a data flow, including Data Source nodes.

To create an Aggregation node:

1. In the Components pane, go to Workflow Editor. If the Components pane is not visible, then in the SQL Developer menu bar, go to View and click Components. Alternately, press Ctrl+Shift+P to dock the Components pane.

2. Expand the Transforms section. Click Aggregate.

3. Drag and drop the node from the Components pane to the Workflow pane.
The node is added to the workflow. The GUI shows that the node has no data associated with it. Therefore, it cannot be run.

4. Right-click the node from which to create the table, and click Connect in the context menu.

5. Right-click the Aggregate node and click Edit.

6. Right-click the Aggregate node and click Run. Monitor the running of the node in the Workflow Jobs. If the Workflow Jobs is not open, then go to View and click Data Miner. Under Data Miner, click Workflow Jobs.

7. After the running of the node is complete, right-click the Aggregate node and select View Data to view the results of the aggregation.

Related Topics

- Editing Aggregate Nodes

Editing Aggregate Nodes

You can define and edit aggregation elements of an Aggregate node in the Edit Aggregate Node dialog box.

To edit an Aggregate node:

1. Double-click or right-click the node and click Edit.

2. To select the Group By columns or Group By expression, click Edit. The Edit Group By dialog box opens.

3. You can define the following:

- To use the Aggregation Wizard, click . The Define Aggregation wizard opens. You can add aggregations one by one.

- To edit an already defined aggregation column, select the aggregation element and click . The Edit Aggregate Element dialog box opens.

- To delete aggregation columns, click .

- To add aggregation columns, click . The Add Column Aggregation dialog box opens.

- To add custom aggregations (an expression), click . The Add Custom Aggregation dialog box opens.

4. When defining the aggregations is complete, click OK.

- Edit Group By
In the Edit Group By dialog box, you can edit the Group By settings for aggregation.

- Define Aggregation
You can define aggregations using the Define Aggregations wizard.

- Edit Aggregate Element
You can define or modify the individual elements of an aggregation.

- Add Column Aggregation
In the Add Column Aggregation dialog box, you can define how a column is aggregated.

- Add Custom Aggregation
In the Add Custom Aggregation dialog box, you can define expressions for custom aggregation.

Edit Group By

In the Edit Group By dialog box, you can edit the Group By settings for aggregation.

The default type isColumn. You can change the type to: Expression.

- If Type is Column, then select one or more columns in the Available Attributes list. You can search the list by name or by data type. Move the selected columns to the Selected Attributes list using the arrows.

- If the Type is Expression, then type an appropriate expression in the Expression box.

Click Validate to validate the expression. After you are done, click OK.

Define Aggregation

You can define aggregations using the Define Aggregations wizard.

To define aggregations:

1. Define the Function to use for aggregation. Available functions depend on the data type of the column that you are aggregating. For example, you can select SUM if you plan to aggregate one or more columns of numeric values. For DATE and TIMESTAMP data types, the functions available are COUNT(), COUNT (DISTINCT(), MAX(), MEDIAN(), MIN(), STATS_MODE()

Click Next.

2. Select one or more Columns to aggregate. You must select columns with a data type compatible with the function that you selected. For example, if the function is SUM, then you must select columns that have numeric data types.

Click Next.

3. Optionally, select a Sub-Group By column for aggregation. Specifying a Sub-Group By column creates a nested table. For example, you could use sub-group by to calculate amount sold per product per customer. The nested table contains columns with data type DM_NESTED_NUMERICALS.

You can select a Sub Group By expression by changing Type to Expression. If you define an Expression, then click Validate to validate the expression.

Click Next.

4. Review the default names for the columns. You can change the names.

5. Review the definitions if necessary. You can click Back to make changes.

6. After you are done, click Finish.

Edit Aggregate Element

You can define or modify the individual elements of an aggregation.

To define or modify the individual element:

1. In Output, you can provide a name. To provide a name, deselect Auto Name and enter a name. By default, Auto Name is selected. Output is the name of the column that holds the results of the aggregation.

2. Select or change the Column that is being aggregated.

3. Select the function to apply to the column. The functions available depend on the data type of the column.

4. Click Edit to define a new Sub-Group By column. The Edit Group By dialog box opens.

5. Once done, click OK.

Related Topics

- Define Aggregation

- Edit Group By

Add Column Aggregation

In the Add Column Aggregation dialog box, you can define how a column is aggregated.

To add an attribute:

1. Click .

2. To provide a name, deselect Auto Name and type in the name. By default, Auto Name is selected. Output is the name of the column that holds the results of the aggregation.

3. Select Columns to aggregate from the list.

4. Select a Function to apply to the column. The functions available depend on the data type of the column. For example, you can specify average (AVG) for a numeric value. For DATE and TIMESTAMP data types, the functions available are COUNT(), COUNT (DISTINCT()), MAX(), MEDIAN(), MIN(), STATS_MODE().

5. To define a Sub Group By column, click Edit. The Edit Group By dialog box opens. It is not necessary to define a Sub Group By column.

6. After you are done, click OK.

Related Topics

- Add Custom Aggregation

- Define Aggregation

- Edit Group By

Add Custom Aggregation

In the Add Custom Aggregation dialog box, you can define expressions for custom aggregation.

To add a custom aggregation, click and follow these steps:

1. Output is the name of column that holds the results of the aggregation. Specify a name.

2. Expression is the expression to add. To define an expression, click to open Expression Builder.

This expression calculates all products bought by a customer and casts the result to a nested data type:

CAST (COLLECT (TO_CHAR (PROD_ID)) AS ODMR_NESTED_VARCHAR2)

3. To define a Sub Group By column, click Edit. The Edit Group By dialog box opens. It is not necessary to define a Sub Group By column.

4. Click Validate to validate the expression.

5. After you are done, click OK.

Related Topics

- Define Aggregation

- Edit Group By

Aggregate Node Properties

In the Properties pane, you can examine and change the characteristics or properties of a node.

To view the properties of a node, click the node and click Properties. If the Properties pane is closed, then go to View and click Properties. Alternately, right-click the node and click Go to Properties.

The Aggregate Node Properties pane has these sections:

- Columns, the columns for the aggregation.

- Cache

- Details

- Cache
The Cache section provides the option to generate a cache for output data.

- Details
The Details section displays the name of the node and any comments about it.

Related Topics

- Editing Aggregate Nodes

Cache

The Cache section provides the option to generate a cache for output data.

The default setting is to not generate the cache to optimize the viewing of results. You can generate the cache.

If you generate the cache, then specify the sampling size as either:

- Number of rows: The default is 2000 rows.

- Percent: The default is 60 percent.

Details

The Details section displays the name of the node and any comments about it.

You can change the name and comments in the fields here:

- Node Name

- Node Comments

Aggregate Node Context Menu

The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

Right-click an Aggregation node. The following options are available in the context menu:

- Connect

- Edit.

- Validate Parents

- Run

- View Data.

- Show Graph

- Force Run

- Deploy

- Show Graph

- Generate Apply Chain

- Cut

- Copy

- Paste

- Extended Paste

- Performance Settings. This opens the Edit Selected Node Settings dialog box, where you can set Parallel Settings and In-Memory settings for the node.

- Select All

- Show Event Log

- Show Runtime Errors. Displayed only if there are errors.

- Show Validation Errors. Displayed if there are validation errors.

- Navigate

Related Topics

- Data Viewer

Data Viewer

You can view data when the Transform node is in a valid state.

To view data, right-click the node and select View Data from the context menu. The data viewer opens.

The data viewer has these tabs:

- Data
The Data tab displays a sample of the data.

- Graph
The Graph tab enables you to create graphs from numeric data.

- Columns
The Column tab is a list of all the columns that are output from the node.

- SQL
In the SQL tab, the SQL Details text area displays the SQL code that generated the data provided by the actual view displayed in the Data tab.

Data

The Data tab displays a sample of the data.

The Data Viewer provides grid display of rows of data either from the sampling defined in the cache or pulled through the lineage of nodes back to the source tables.

The display is controlled with:

- Refresh: To refresh the display, click .

- View: Enables you to select either Cached Data or Actual Data.

- Sort: Displays the Select Column to Sort By dialog box.

- Filter: Enables you to type in a WHERE clause to select data.

- Select Column to Sort By

Select Column to Sort By

The Select Column to Sort By dialog box enables you to:

- Select multiple columns to sort.

- Determine the column ordering.

- Determine ascending or descending order by column.

- Specify Nulls First so that null values appear ahead of real data values.

The sort order is preserved until you clear it.

Column headers are also sort-enabled to provide a temporary override to the sort selection.

Graph

The Graph tab enables you to create graphs from numeric data.

Related Topics

- Graph Node

Columns

The Column tab is a list of all the columns that are output from the node.

The display in the tab depends on the following conditions:

- If the node has not run, then the Table or View structure provided by the database is displayed.

- If the node has run successfully, then the structure of the sampled table is displayed. This is based on the sampling defined when the node was specified.

For each column, the following are displayed:

- Name

- Data type

- Mining type

- Length

- Precision

- Scale (for floating point)

- Column ID

There are several filtering options that limit the columns displayed. The filter settings with the (or)/(and) suffixes allow you to enter multiple strings separated by spaces. For example, if the Name/Data Type/Mining Type(or) is selected, then the filter string A B produces all columns where the name or the data type or the mining type starts with the letter A or B.

SQL

In the SQL tab, the SQL Details text area displays the SQL code that generated the data provided by the actual view displayed in the Data tab.

The SQL can be a stacked expression that includes SQL from parent nodes, depending on what lineage is required to access the actual data.

You can perform the following tasks:

- Copy and run the SQL query in a suitable SQL interface. The following options are enabled:

- Select All (Ctrl+A)

- Copy (Ctrl+C)

- Search texts. The Search control is a standard search control that highlights matching text and searches forward and backward.

Expression Builder

Expression Builder helps you to enter and validate SQL expressions, such as constraints for filters.

An expression is a SQL statement or clause that transforms data or specifies a restriction. Expression Builder displays the available columns, provides a selection of functions and commonly used operators, and validates expressions.

To build and validate expressions in Expression Builder:

1. Click in the Add Custom Transform dialog box. The Expression Builder dialog box opens.

2. The Expression Builder dialog box has the following components:

- Attributes: Lists attributes (columns) in the source data. To insert an attribute into the query that you are creating in the Expression box, or to replace characters that you selected, double-click the attribute at the current character position.

- Functions: Lists the commonly used SQL Functions, divided into folders. Double-click a folder to view the functions listed there. To insert a function into the expression at the current character position or to replace characters that you selected, double-click the function.

- Expression: The expression that you create is displayed in the Expression box. You can create an expression in any one of the following ways:

- Type the expression in the Expression box directly.

- Add Attributes and Functions by double-clicking them in the Attributes tab and the Functions tab respectively.

To add an operator into the expression, click the operator.

- Commonly used operators are listed in below the Expression box. Click the appropriate operator, indicated by its symbols. If you prefer, you can enter an operator directly into the Expression box. Table 7-1 lists the operators that you can enter:

Table 7-1 Commonly Used Operators

To Enter this Operator

Click

Less than

<

Greater than

>

Less than or equal to

... for the symbol <=

Greater than or equal to

... for the symbol >=

Not equal to

!=

Equal to

=

Or (Logical Or)

...

And

...

Left parenthesis

(

Right parenthesis

)

Parallel symbol

||

Add

+

Minus

-

Multiply

*

Divide

/

Percent

%

- Validation Results text area (read-only): Displays the validation results.

- Validate: Click Validate to validate the expression in the Expression box. The results appear in Validation Results.

3. When you have finished creating the expression, click OK.

- Functions
Expression Builder includes a variety of functions that can be applied to character, numeric, and date data.

Related Topics

- Functions

Functions

Expression Builder includes a variety of functions that can be applied to character, numeric, and date data.

There are functions that support most of the common data preprocessing required for data mining, including missing values treatment. To see a list of the available functions, expand the category of interest.

Functions are divided into the following categories:

- Character: Includes concatenate, trim, length, substring, and others.

- Conversion: Converts to character, date, number, and others.

- Date: Calculates next day, inserts time stamp, truncates, rounds, and performs other date operations.

- Numeric: Includes functions such as absolute value, ceiling, floor, trigonometric functions, hyperbolic functions, logarithms, and exponential values.

- Analytical: Performs analytical functions.

- NULL Value Substitution: For dates, characters, and numbers.

The notation for the functions is that of SQL.

Related Topics

- Oracle Database SQL Language Reference

Filter Columns Node

Filter Columns filters out columns so that the columns are not used in subsequent workflow calculations.

For example, you might want to filter out or ignore columns that have more than 94 percent Null values.

Optionally, you can identify important attributes.

Filter Columns requires analysis after it runs. The transformation makes recommendations. You can decide which recommendation to accept.

Filter Columns can run in parallel.

These topics describe Filter Columns nodes:

- Creating Filter Columns Node
You create a Filter Columns node to filters out columns so that the columns are not used in subsequent workflow calculations.

- Editing Filter Columns Node
In the Edit Filter Columns Node dialog box, you can define or edit the filter performed by the Filter Columns node.

- Filter Columns Node Properties
In the Properties pane, you can examine and change the characteristics or properties of a node.

- Filter Columns Node Context Menu
The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

Related Topics

- Attribute Importance

Creating Filter Columns Node

You create a Filter Columns node to filters out columns so that the columns are not used in subsequent workflow calculations.

Before you define filter columns, you must identify a Data Source node and decide whether to find important attributes.

To define Filter Columns:

1. In the Components pane, go to Workflow Editor. If the Components pane is not visible, then in the SQL Developer menu bar, go to View and click Components. Alternately, press Ctrl+Shift+P to dock the Components pane.

2. Expand the Transforms section and click Filter Columns.

3. Drag and drop the node from the Components pane to the Workflow pane.
The node is added to the workflow. The GUI shows that the node has no data associated with it. Therefore, it cannot be run.

4. Connect the Data Source node to the Filter Columns node:

a. Right-click the Data Source node, and select Connect.

b. Draw a line to the Filter Columns node and click again.

5. Right-click the Filter Columns node and click Edit.

6. Right-click the Filter Columns node and click Run. Monitor the running of the node in Workflow Jobs. If the Workflow Jobs is not open, then in View, go to Data Miner and click Workflow Jobs.

7. After the running of the node is complete, right-click the Filter Columns node and select View Data to view the results of the filtered columns.

Related Topics

- Editing Filter Columns Node

Editing Filter Columns Node

In the Edit Filter Columns Node dialog box, you can define or edit the filter performed by the Filter Columns node.

For supervised cases, the icon next to the attribute name indicates that it is the target attribute. Also, in the top right corner, the target attribute is indicated by this icon. You can perform the following tasks:

- View attribute importance: Select Show Attribute Importance to view the attribute importance in the table columns. The information is displayed in the columns Rank and Importance.

- View data quality: Select Show Data Quality to display Filter Columns settings in terms of percent age of null values (% Null), percentage of values that are unique (% Unique), and percentage of constants (% Constants).

- Exclude Columns: You can exclude columns when you first edit the Filter Columns node.

- Edit or view Filter Columns settings: You can edit or view Filter Columns settings when you first edit the Filter Columns node.

- Calculate important attributes. Click Settings to enable attribute importance.

- Evaluate hints and decide which columns to filter out. Additional information is available in the form of Hints after the Filter Columns node runs.

- Apply recommended settings to selected settings, click

- Revert output settings. After applying recommended settings to another settings, if you want to revert to the original settings, then click

- Explore pairwise dependency in the dataset: Click to view the pairwise dependency between attributes in the Explore Dependencies dialog box.

- Edit dependency settings: Click to view and edit the dependency settings in the Predictor Dependencies dialog Box.

Note:
This option is enabled only after the node is run in Supervised mode.

- Exclude Columns
By default, all columns are selected for output. That is, all columns are passed to the next node in the workflow.

- Define Filter Columns Settings
You can create and edit Filter Columns settings in the Define Filter Columns Settings dialog box.

- Explore Dependencies
In the Explore Dependencies dialog box, you can view the pairwise dependency between two attributes.

- Predictor Dependencies
You can view the dependency of the selected attribute with other attributes, and set them to be considered as output in the Predictor Dependencies window.

- Performing Tasks After Running Filter Columns Node
Lists the tasks that you can perform after you run the Filter Columns node.

- Columns Filter Details Report
When you run the Filter Columns node, the Columns Filter Details Report is generated in the Edit Column Details dialog box.

- Attribute Importance
Oracle Data Miner ranks the attributes by significance in determining the target value.

Exclude Columns

By default, all columns are selected for output. That is, all columns are passed to the next node in the workflow.

- To exclude a column, click . The arrow is crossed out, indicated by . The excluded column is ignored, that is, it is not passed on.

- To view or change settings, click Settings. The Define Filter Columns Settings dialog box opens.

Related Topics

- Define Filter Columns Settings

Define Filter Columns Settings

You can create and edit Filter Columns settings in the Define Filter Columns Settings dialog box.

There are three kinds of settings:

- Data Quality: Allows Filter Columns settings in terms of percent age of null values, percentage of values that are unique, and percentage of constants. The default values for Data Quality are specified in preferences. You can change the default. You can specify the following Data Quality criteria:

- % Nulls less than or equal: Indicates the largest acceptable percentage of Null values in a column of the data source. You may want to ignore columns that have a larger percentage of Null values. The default value is 95 percent.

- % Unique less than or equal: Indicates the largest acceptable percentage of values that are unique in a column of the data source. If a column contains many unique values, then it may not contain useful information for model building. The default value is 95 percent.

- % Constant less than or equal: Indicates the largest acceptable percentage of constant values in a column of the data source. If most of the values in a column is the same, then the column may not be useful for model building.

- Attribute Importance: Enables you to build an attribute importance model to identify important attributes. By default, this setting is turned OFF. Filter Columns does not calculate Attribute Importance.

- Target: The value for which to find important attributes. Usually the target of a classification problem.

- Importance Cutoff: A number between 0 and 1.0. This value identifies the smallest value for importance that you want to accept. If the importance of an attribute is a negative number, then that attribute is not correlated with the target, so the cutoff should be nonnegative. The default cutoff is 0. The rank or importance of an attribute enables you to select the attribute to be used in building models.

- Top N: The maximum number of attributes. The default is 100.

- Attribute Dependency: Select this option to generate pairwise dependency information. In case of supervised mode, you can modify the output columns that are used in the result. Attribute Dependency is selected by default if Attribute Importance is selected.

Note:
You must select Attribute Importance to generate Attribute Dependency.

Sampling (Data Quality and Attribute Importance): Enables you to select the number of rows, that could be system determined or user specified. The default values for sampling are specified in preferences. You can change the default or even turn off sampling. The default sample size is 10,000 records.

Related Topics

- Filter Columns

- Attribute Importance

Explore Dependencies

In the Explore Dependencies dialog box, you can view the pairwise dependency between two attributes.

You must select Attribute Importance in order to generate Attribute Dependency.

To view the pairwise dependency between attributes:

1. In the Attribute field, enter the name of the attribute for which you want to view its dependency with other attributes in pairs. The list of all pairwise dependencies that contains the selected name is displayed.

2. In the Fetch Size field, provide a number to determine the fetch size of the data in the table and click Query.

3. In the Sort By Dependency field, select:

- Ascending: To view the attribute dependency value in ascending order.

- Descending: To view the attribute dependency value in ascending order.

4. Click Query.
It lists the dependency of the attribute with other attributes in pairs.

5. Click Close.

Predictor Dependencies

You can view the dependency of the selected attribute with other attributes, and set them to be considered as output in the Predictor Dependencies window.

The Predictor Dependencies window comprises two panels.

The top panel or the Master Table displays the attributes that contain pairwise dependency. It displays the following columns for the selected attribute:

- Columns

- Importance

- Weighted Dependency

- Output: Indicates whether the attribute is considered as output or not. Click the green arrow to change it to so that the attribute is not considered as output.

The bottom panel or the Detail Table displays the attributes that are dependent with the selected attribute in the top panel. Select Show all Columns to view all attributes in the data source. If Show all Columns is deselected, then only the attributes present in the Master Table are displayed. It displays the following columns for the selected attribute:

- Columns

- Dependency

- Output: Indicates if the column is used as an output or not. The green arrow indicates that the column is used in output, and the green arrow with a red cross indicates that it is not considered in the output.

You can search attributes and modify the fetch size.

1. To search an attribute either in the top panel or bottom panel, enter an attribute name and click .
The columns of the selected attribute are displayed in the respective panels.

2. In the Fetch Size field in the bottom pane, select a number to display that many number attributes. For example, if you enter 20, then only 20 attributes will be displayed.

3. After you enter the number, click Query.
The attributes and their dependency are displayed in the lower pane.

4. Click OK.

Performing Tasks After Running Filter Columns Node

Lists the tasks that you can perform after you run the Filter Columns node.

You can perform the following tasks:

- View hints: To view hints, double-click the Filter Columns node. The Edit Column Filter Details Node dialog box displays hints, to indicate attributes that did not pass the data quality checks. For more information, click .

- Summary information is displayed about data quality.

- Values are indicated graphically in the Data Viewer.

If you specified Attribute Importance, then:

- Hints indicate attributes that do not have the minimum importance value.

- The importance of each column is displayed.

- Exclude columns: Go to the Output column for the attribute and click . The icon in the Output column changes to . The selected columns are ignored or excluded, which means that the columns are not for subsequent nodes. It is not necessary to run the nodes again.

- Accept recommendations:

- For several recommendations, select the attributes and click .

- For all recommendations, type Ctrl+A and click .

- Apply recommended output settings: Attributes that have Hints are not passed on. Attributes that have no hints are not changed and they are passed on.

- Create a Table or View node: The output of this node is a data flow. To create a table containing the results, use a Create Table or View Node.

Related Topics

- Columns Filter Details Report

- Create Table or View Node

Columns Filter Details Report

When you run the Filter Columns node, the Columns Filter Details Report is generated in the Edit Column Details dialog box.

Columns of the grid summarize the data quality information.

The default settings show both Attribute Importance and Data Quality.

- If Attribute Importance is selected, then following are displayed:

- Rank

- Importance

- If Data Quality is selected, then the following columns are displayed:

- % Null

- % Unique

- % Constant

The Hints column in the grid indicates the columns in the data set that do not pass data quality or do not meet the minimum importance value.

Bar graphs give a visual indication of values.

For example, if the percentage of Null values is larger than the value specified for % Nulls less than or equal, then a hint is generated that indicates that the percentage of Null values is exceeded. If the percent of NULL values is very large for a column, then you may want to exclude that column.

Attribute Importance

Oracle Data Miner ranks the attributes by significance in determining the target value.

If a data set has many attributes, then it is likely that not all attributes contribute to a predictive model. Some attributes may simply add noise, that is, they actually detract from the predictive value of the model. You can then filter out attributes that are not important in determining the target value.

Using fewer attributes does not necessarily result in loss of predictive accuracy. Using too many attributes, can affect the model and degrade its performance and accuracy. Mining using the smallest number of attributes can save significant computing time and may build better models.

The following are applicable for Attribute Importance:

- Attribute Importance is most useful with Classification.

- The target for Attribute Importance in Filter Column should be the same as the target of the Classification model that you plan to build.

- Attribute Importance calculates the rank and importance for each attribute.

- The rank of an attribute is an integer.

- The Importance of an attribute is a real number, which can be negative.

Specify these values for attribute importance:

- Target: The value for which to find important attributes. Usually the target of a classification problem.

Note:
For Unsupervised Attribute Importance, the Target is set to Not Specified, if the target is not specified by the user.

- Importance Cutoff: A number between 0 and 1.0. This value identifies the smallest value for importance that you want to accept. If the importance of an attribute is a negative number, then that attribute is not correlated with the target, so the cutoff should be nonnegative. The default cutoff is 0. The rank or importance of an attribute enables you to select the attribute to be used in building models.

- Top N: The maximum number of attributes. The default is 100.

- Select a Sample technique for the Attribute Importance calculation. The default is system determined. You can also select Stratified or Random.
System determined has a stratified cutoff value with a default value of 10.

- If the distinct count of the selected column is greater than the cutoff value, then use random sampling.

- If the distinct count of the selected column is less than or equal to the cutoff value, then use stratified sampling.

Certain combinations of target and sampling may result in performance problems. You are given a warning if there is a performance problem.

- Attribute Importance Viewer
To view an Attribute Importance model, build a Filter Columns node with Attribute Importance selected.

Attribute Importance Viewer

To view an Attribute Importance model, build a Filter Columns node with Attribute Importance selected.

Right-click the node and select View Data. Results are displayed in a new Filter Columns Details tab. The viewer has these tabs:

- Attribute Importance: Lists those attributes with Importance greater than or equal to 0. Attributes are listed in order of rank from lowest rank (most important) to highest rank. The tab also displays the data type of each attribute. A blue bar indicates the rank. You can sort any of the columns by clicking the column heading.

- To filter columns, that is, to limit the number of columns displayed, use .

- To clear filter definition, click . You can also search by name, type, rank or importance.

- Data: Lists the important attributes in order of importance, largest first. For each attribute rank and importance, values are listed. Only those attributes with an importance value greater than or equal to 0 are listed.

- Columns: Displays the columns created by Attribute Importance, that is attribute name, rank, and importance value.

- SQL: This is the SQL that generates the details.

Filter Columns Node Properties

In the Properties pane, you can examine and change the characteristics or properties of a node.

To view the properties of a node, click the node and click Properties. If the Properties pane is closed, then go to View and click Properties. Alternately, right-click the node and click Go to Properties.

Filter Columns Node Properties has these sections:

- Columns: Displays the columns of the data source. After the node runs, hints are displayed.

- Filters.

- Cache

- Details

Related Topics

- Define Filter Columns Settings

Filter Columns Node Context Menu

The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

Right-click the Filter Columns node. The following options are available in the context menu:

- Connect

- Edit.

- Validate Parents

- Run

- View Data.

- Force Run

- Deploy

- Show Graph

- Generate Apply Chain

- Cut

- Copy

- Paste

- Extended Paste

- Performance Settings. This opens the Edit Selected Node Settings dialog box, where you can set Parallel Settings and In-Memory settings for the node.

- Select All

- Show Event Log

- Show Runtime Errors. Displayed if there are errors.

- Show Validation Errors. Displayed if there are validation errors.

- Navigate

Related Topics

- Editing Filter Columns Node

Filter Columns Details

The Filter Columns Details node creates a data flow that consists of the result of Attribute Importance.

For each attribute, the rank and importance values are listed.

Note:

Filter Columns Details must be connected to a Filter Columns Node that has Attribute Importance selected in Settings. Otherwise, the Filter Columns Details node is Invalid.

Filter Columns Details can run in parallel.

This section consists of the following topics:

- Creating the Filter Columns Details Node
You create a Filter Columns Details node to create a data flow that consists of the result of Attribute Importance.

- Editing the Filter Columns Details Node
You can define or edit the filter performed by the Filter Columns node.

- Filter Columns Details Node Properties
In the Properties pane, you can examine and change the characteristics or properties of a node.

- Filter Columns Details Node Context Menu
The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

Related Topics

- About Oracle Database In-Memory

- About Parallel Processing

Creating the Filter Columns Details Node

You create a Filter Columns Details node to create a data flow that consists of the result of Attribute Importance.

Before creating the Filter Columns Details node, you must identify a Filter Columns node, where Attribute Importance is selected in the Settings.

To create a Filter Columns Details node:

1. In the Components pane, go to Workflow Editor. If the Components pane is not visible, then in the SQL Developer menu bar, go to View and click Components. Alternately, press Ctrl+Shift+P to dock the Components pane.

2. Expand the Transform section and click Filter Columns Details.

3. Drag and drop the node from the Components pane to the Workflow pane.
The node is added to the workflow. The GUI shows that the node has no data associated with it. Therefore, it cannot be run.

4. Connect the Filter Columns node to the Filter Columns Details node:

a. Right-click the Filter Columns node, and select Connect.

b. Draw a line to the Filter Columns Details node and click again.

5. You can right-click the Filter Columns Details node and select Edit. In this release there are no options to select.

6. Right-click the Filter Columns Details node and select Run. Monitor the running of the node in the Workflow Jobs. If the Workflow Jobs is not open, then go to View and click Data Miner. Under Data Miner, click Workflow Jobs.

7. After the running of the node is complete, right-click the Filter Columns Details node and select View Data to view the results.
The output of this node is a data flow. To create a table containing the results, use a Create Table or View node.

Note:
Filter Columns Details consists of the results of Attribute Importance only. It does not contain any information about Data Quality.

Related Topics

- Create Table or View Node

Editing the Filter Columns Details Node

You can define or edit the filter performed by the Filter Columns node.

You can perform the following tasks:

- Exclude Columns: You can exclude columns when you first edit the Filter Columns node.

- Edit or view Filter Columns settings: You can edit or view Filter Columns settings when you first edit the Filter Columns node.

- Calculate important attributes: Click Settings to enable Attribute Importance.

- Evaluate: Evaluate hints and decide which columns to filter out. Additional information.

Filter Columns Details Node Properties

In the Properties pane, you can examine and change the characteristics or properties of a node.

To view the properties of a node, click the node and click Properties. If the Properties pane is closed, then go to View and click Properties. Alternately, right-click the node and click Go to Properties.

The Filter Columns Node Properties has these sections:

- Output: The only valid value is Attribute Importance, which is the default.
A grid lists the data types of ATTRIBUTE_NAME, RANK, and IMPORTANCE_VALUE.

- Cache

- Details

Filter Columns Details Node Context Menu

The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

Right-click the Filter Columns Details node. The following options are available in the context menu:

- Connect

- Edit.

- Validate Parents

- Run

- View Data

- Force Run

- Deploy

- Show Graph

- Generate Apply Chain

- Cut

- Copy

- Paste

- Extended Paste

- Performance Settings. This opens the Edit Selected Node Settings dialog box, where you can set Parallel Settings and In-Memory settings for the node.

- Select All

- Show Event Log

- Show Runtime Errors. Displayed if there are errors.

- Show Validation Errors. Displayed if there are validation errors.

- Navigate

Related Topics

- Attribute Importance Viewer

Filter Rows

A Filter Rows node enables you to select rows by specifying a SQL statement that describes the rows.

For example, to select all rows where CUST_GENDER is F, specify: CUST_GENDER = 'F'

You can either write the SQL expression directly or use Expression Builder.

Filter Rows can run in parallel.

This section consists of the following topics:

- Creating a Filter Rows Node
You create a Filter Rows node to select rows by specifying SQL statements, as applicable.

- Edit Filter Rows
The Edit Filter Rows dialog box defines or edits the filter performed by the Filter Rows node.

- Filter Rows Node Properties
In the Properties pane, you can examine and change the characteristics or properties of a node.

- Filter Rows Node Context Menu
The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

Related Topics

- About Parallel Processing

- About Oracle Database In-Memory

- Expression Builder

Creating a Filter Rows Node

You create a Filter Rows node to select rows by specifying SQL statements, as applicable.

Identify a Data Source node. Identify or create the node to filter. The node can be any node that provides a data flow, including Data Source nodes.

To define a Filter Rows node:

1. In the Components pane, go to Workflow Editor. If the Components pane is not visible, then in the SQL Developer menu bar, go to View and click Components. Alternately, press Ctrl+Shift+P to dock the Components pane.

2. Expand the Transform section and click Filter Rows.

3. Drag and drop the node from the Components pane to the Workflow pane.
The node is added to the workflow. The GUI shows that the node has no data associated with it. Therefore, it cannot be run.

4. Connect the Data Source node to the Filter Rows node:

a. Move the cursor to the Data Source node.

b. Right-click the Data Source node, and select Connect.

c. Drag the line to the Filter Rows node and click again.

5. Right-click the Filter Rows node and select Edit. Use the Edit Filter Rows dialog box to define the filter.

6. Right-click the Filter Rows node and select Run. Monitor the running of the node in the Workflow Jobs. If the Workflow Jobs is not open, then go to View and click Data Miner. Under Data Miner, click Workflow Jobs.

7. After the running of the node is complete, right-click the Filter Rows node and select View Data to view the results of the Filter Rows.

Related Topics

- Edit Filter Rows

Edit Filter Rows

The Edit Filter Rows dialog box defines or edits the filter performed by the Filter Rows node.

The Edit Filter Rows dialog has two tabs:

- Filter
The filter is one or more SQL expressions that describe the rows to select.

- Columns
The Columns tab lists the output columns.

Filter

The filter is one or more SQL expressions that describe the rows to select.

To create or edit a filter:

1. Open the Expression Builder by clicking .

2. Write the SQL query to use for filtering.

3. After specifying an expression, you can delete it. Select it and click .

4. After you are done, click OK. Data Miner validates the expression.

Related Topics

- Expression Builder

Columns

The Columns tab lists the output columns.

You can filter in several ways.

Click OK when you have finished. Data Miner validates the expression.

Related Topics

- Filter

Filter Rows Node Properties

In the Properties pane, you can examine and change the characteristics or properties of a node.

To view the properties of a node, click the node and click Properties. If the Properties pane is closed, then go to View and click Properties. Alternately, right-click the node and click Go to Properties.To view the properties of the Filter Rows node:

The Filter Rows node Properties tab has these sections:

- Filter: The SQL expression created with Edit Filter Rows. You can modify the expression in the Properties by clicking .

- Columns: The output data columns. For each column, name, alias (if any), and data types are listed.

- Cache

- Details

Related Topics

- Edit Filter Rows

- Cache

- Details

Filter Rows Node Context Menu

The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

Right-click the Filter Rows node. The following options are available in the context menu:

- Connect

- Edit

- Validate Parents

- Run

- View Data

- Force Run

- Deploy

- Show Graph

- Generate Apply Chain

- Cut

- Copy

- Paste

- Extended Paste

- Performance Settings. This opens the Edit Selected Node Settings dialog box, where you can set Parallel Settings and In-Memory settings for the node.

- Select All

- Show Event Log

- Show Runtime Errors. Displayed if there are errors.

- Show Validation Errors. Displayed if there are validation errors.

- Navigate

Related Topics

- Data Viewer

Join

A Join node combines data from two or more Data Source nodes into a new data source.

Technically, a Join node is a query that combines rows from two or more tables, views, or materialized views. For example, a Join node combines tables or views (specified in a FROM clause), selects only rows that meet specified criteria (WHERE clause), and uses projection to retrieve data from two columns (SELECT statement).

Join can run in parallel.

This section contains the following topics:

- Create a Join Node
You create a Join node combines data from two or more Data Source nodes into a new data source.

- Edit a Join Node
In the Edit Join Node dialog box, you can specify or change the characteristics of the models to build.

- Join Node Properties
In the Properties pane, you can examine and change the characteristics or properties of a node.

- Join Node Context Menu
The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

Related Topics

- Oracle Database Concepts

Create a Join Node

You create a Join node combines data from two or more Data Source nodes into a new data source.

Specify two or more Data Source nodes and at least one output column.

Joins are sometimes very slow. If you materialize the join input as an indexed table, then the join may be much faster. The output of a Join node is a data flow.

Note:
To materialize a join input as a table or view, connect it to a Create Table or View node.

To join two or more Data Source nodes:

1. In the Components pane, go to Workflow Editor. If the Components pane is not visible, then in the SQL Developer menu bar, go to View and click Components. Alternately, press Ctrl+Shift+P to dock the Components pane.

2. Drag and drop the node from the Components pane to the Workflow pane.
The node is added to the workflow. The GUI shows that the node has no data associated with it. Therefore, it cannot be run.

3. Connect the Data Source nodes to be joined to the Join node:

a. Move the cursor to one of the nodes to join.

b. Right-click the node, and select Connect.

c. Draw a line to the Join node and click again.

d. Repeat until all nodes to join are connected to the Join node.

4. Right-click the Join node and select Edit. Use the Edit Join Node option to define the Join node.

5. Right-click the Join node and select Run. Monitor the running of the node in the Workflow Jobs. If the Workflow Jobs is not open, then go to View and click Data Miner. Under Data Miner, click Workflow Jobs.

6. After the running of the node is complete, right click the Join node and select View Data to view the results of the join.
You can also define the join and view results through the Join node properties.

Related Topics

- Create Table or View Node

Edit a Join Node

In the Edit Join Node dialog box, you can specify or change the characteristics of the models to build.

You can define a Join node in one of the following ways:

- Either double-click the Join node, or right-click the node and select Edit. Click the Join tab.

- Select the node. In the Properties pane, select the Join tab. Click .

In either case, the Edit Join Node dialog box opens.

- Edit Join Node
In the Edit Join Node dialog box, you can add columns, define filters and resolve issues related to the join specifications.

- Edit Columns
The default setting is to use Automatic Settings for the list of columns displayed.

- Edit Output Data Column
In the Edit Output Data Column dialog box, you can exclude columns from the output.

- Resolve
In the Resolve dialog box, you can resolve issues related to join specifications which may become invalid due to different reasons.

Edit Join Node

In the Edit Join Node dialog box, you can add columns, define filters and resolve issues related to the join specifications.

Click the Join tab if it is not displayed. In the Edit Join Node dialog box, you can perform the following tasks:

- To add a new Join column, click . The Edit Join Column dialog box opens.

1. In the Edit Join Column dialog box, select the Data Sources—Source 1 and Source 2. You can search columns in either Source by Name or by Data Type.

2. Select one entry in Source 1 and the corresponding entry in Source 2.

3. Click Add. Data Miner selects an appropriate Join Type. Column 1 (from Source 1), Column 2 (from Source 2), and the Join type are displayed in a grid. You can search this grid by Column 1, Column 2, or Join Type.

4. After you are done, click OK.

- To select the columns in the Join, click the Column tab to display Edit Columns dialog box.

- To define a filter for the Join, select the Filter tab and enter an appropriate SQL expression. You can also use SQL Worksheet (part of SQL Developer) to write a filter.

If there are problems with the join, for example, if one of the data nodes is no longer connected to the Join node, then an information indicator is displayed as follows:

Click Resolve Issues. This opens the Resolve dialog box.

Related Topics

- Edit Columns

- Resolve

Edit Columns

The default setting is to use Automatic Settings for the list of columns displayed.

To select columns, go to the Columns tab of Edit Joins Details in one of the following ways:

- Right-click the Join node and select Edit. Then click Columns.

- Select the Join node. In the Properties pane, click Columns.

To make changes, deselect Automatic Settings. You can perform the following tasks:

- Edit the list of columns: Open the Edit Output Data Column dialog box and click .

- Delete a column from the output: Select the column and click .

If the node was run, then you must run it again.

Related Topics

- Edit Output Data Column

Edit Output Data Column

In the Edit Output Data Column dialog box, you can exclude columns from the output.

The default setting is to include all columns from both tables in the output.

To remove a column from the output:

1. Move the columns from the Selected Attributes list to the Available Attributes list.

2. Click OK.

Resolve

In the Resolve dialog box, you can resolve issues related to join specifications which may become invalid due to different reasons.

When a Data Source node is disconnected from a Join node, all the join specifications for that node are retained and are marked as Invalid. Before you run the Join node, you must resolve the issues. The Resolve dialog box provides two ways to resolve the join issues:

- Remove: Removes all invalid entries from all specifications (Apply and Data).

- Resolve: Displays a grid that enables you to associate an unassigned node with a missing node. The missing nodes are listed in the grid, and an action is suggested.

Join Node Properties

In the Properties pane, you can examine and change the characteristics or properties of a node.

To view the properties of a node, click the node and click Properties. If the Properties pane is closed, then go to View and click Properties. Alternately, right-click the node and click Go to Properties.To view the properties of a Join node:

The Properties pane for a Join node has these sections:

- Join: Defines the Join.

- Columns: Displays the output columns of the Join. For each column, name, node, alias (if any) and data type are displayed. Up to 1000 columns are displayed.

- Filter: Filters results by defining filter conditions using the Expression Builder. Open the Expression Builder by clicking .

- Cache

- Details

Related Topics

- Cache

- Details

- Expression Builder

Join Node Context Menu

The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

Right-click the Join node. The following options are available in the context menu:

- Connect

- Edit

- Validate Parents

- Run

- View Data

- Force Run

- Deploy

- Show Graph

- Generate Apply Chain

- Cut

- Copy

- Extended Paste

- Paste

- Performance Settings. This opens the Edit Selected Node Settings dialog box, where you can set Parallel Settings and In-Memory settings for the node.

- Select All

- Show Event Log

- Show Runtime Errors. Displayed if there are errors.

- Show Validation Errors. Displayed if there are validation errors.

- Navigate

Related Topics

- Data Viewer

JSON Query

The support for the JSON data format in Oracle Data Miner (SQL Developer 4.1 and later ) is facilitated by the JSON Query node.

JSON or JavaScript Object Notation is a data format, that enables users to store and communicate sets of values, lists, and key-value mappings across systems.

The JSON Query node projects the JSON data format to the relational format. It supports only one input data provider node, such as Data Source node. You can perform the following tasks in the JSON Query node:

- Select any JSON attributes in the source data to project it as relational data

- Select relational columns in the source data to project it as relational data

- Define aggregation columns on JSON data

- Preview output data

- Construct a JSON Query based on user specifications

Note:

JSON Query Node is supported on Oracle Database 12.1.0.2 and later.

- Create JSON Query Node
A JSON Query node should be connected to an input provider node, such as a Data Source node.

- JSON Query Node Editor
In the Edit JSON Query Node dialog box, you can only operate on the input columns that are pseudo JSON types.

- JSON Query Node Properties
In the Properties pane, you can examine and change the characteristics or properties of a node.

- JSON Query Node Context Menu
The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

- Data Types and their Supported Operators
Lists the data types and their supported operators for JSON data type.

Create JSON Query Node

A JSON Query node should be connected to an input provider node, such as a Data Source node.

To run the nodes successfully, the input provide node must contain JSON data.

To create a JSON Query node:

1. In the Components pane, go to Workflow Editor. If the Components pane is not visible, then in the SQL Developer menu bar, go to View and click Components. Alternately, press Ctrl+Shift+P to dock the Components pane.

2. In the Transforms section, click JSON Query Node.

3. Drag and drop the node from the Components pane to the Workflow pane.
The node is added to the workflow. The GUI shows that the node has no data associated with it. Therefore, it cannot be run.

4. Right-click the node, for example a Data Source node, from which to create the connection and click Connect in the context menu.

5. Draw a line from the selected node to the JSON Query node and click again. This connects the JSON Query node to the Data Source node.

JSON Query Node Editor

In the Edit JSON Query Node dialog box, you can only operate on the input columns that are pseudo JSON types.

To open the Edit JSON Query Node dialog box:

- Double-click the JSON Query node, or

- Right-click the node and click Edit.

The Edit JSON Query Node dialog box consists of the following tabs:

- JSON
In the JSON tab, you can select JSON data, specify filters on attributes and so on.

- Additional Output
In the Additional Output tab, you can select relational columns in the source data for the output.

- Aggregate
You can define aggregation column definitions on JSON attributes in the Aggregate tab.

- Preview
You can preview the node output in the Preview tab.

JSON

In the JSON tab, you can select JSON data, specify filters on attributes and so on.

In the Columns drop-down list, only the input columns containing JSON data (pseudo JSON data types) are listed. You can also specify filters on attributes for the following data type: ARRAY, BOOLEAN, NUMBER and STRING. The filters are applied to the data in hierarchical order by using the logical operators as specified by you, such as Match All or Any. Select an input column from the drop-down list.

Click to set and apply filter settings. The Filter Settings dialog box opens.

The JSON tab consists of the following:

- Structure
In the Structure tab, the JSON data structure for the selected column is displayed.

- Data
The Data tab displays the JSON data that is used to create the JSON structure.

- Filter Settings
In the Filter Settings dialog box, you can specify filters on attributes for the data type: ARRAY, BOOLEAN, NUMBER and STRING.

Structure

In the Structure tab, the JSON data structure for the selected column is displayed.

The structure or the Data Guide table must be generated in the parent source node, for example, Data Source node. If the structure is not found, then a message is displayed to communicate the same.

The following information about the data structure is displayed:

- JSON Attribute: Displays the generated JSON structure in a hierarchical format. You can select one or more attribute to import. When you select a parent attribute, all child attributes are automatically selected.

- JSON Data Type: Displays the JSON data types of all attributes, derived from JSON data.

- Unnest: All attributes inside an array are unnested in a relational format. By default, the Unnest option is enabled. When the Unnest option for an array attribute is disabled, then:

- The child attributes are displayed but cannot be selected.

- If the array attribute is selected for output, then the output column will contain JSON representation of the array.

You can perform the following tasks in the Structure tab:

- Set viewing preferences: In the View drop-down list, you can set your viewing preferences by clicking any of the following:

- All: To view all attributes.

- Only Selected: To view only the selected attributes.

- Only with Filters: To view only those attributes, along with their parent attributes, that have filter definitions applied.

- Selected and with Filters: To view only the selected attributes that have filter definitions.

- Select attributes: To select one or more attribute, click the check box against the attribute.

Note:

When you select an attribute, the parent attribute is selected automatically. If you select a parent attribute, then all its children attributes are automatically selected. This is applicable if none of its immediate child attribute is part of the group selection.

- Copy filter: Click to copy the filter of an attribute to local cache. You can then apply the copied filter to another attribute with the same data type by using the paste option.

Note:

This option is enabled only if the selected attribute has a filter definition.

- Paste filter: After copying a filter from an attribute, click the attribute to which you want to paste the filter and click .

Note:

Attributes with compatible data type can accept the copied filter. For example, a filter that is copied from an attribute with NUMBER data type can be pasted to attributes with NUMBER data type only.

- Clear filter: Select the attribute from which you want to remove the filter and click .

- Edit filter: You can add or edit filter for any of the attribute type STRING, NUMBER, BOOLEAN, and ARRAY, by using the in place edit option. To edit or add a filter to an attribute:

1. Select the attribute and click . Alternately, select the attribute and double click the corresponding Filter column cell. The in place edit option for the selected attribute is enabled with the applicable operators listed in the drop-down list. Select an operator from the drop-down list.

2. Select the value from the corresponding field by clicking . The Find Values dialog box opens.

3. In the Find Values dialog box, select the values and click OK. The filter and the values are now visible in the JSON Query Node Editor dialog box, as displayed below:

To complete the editing, press the Enter key. To cancel editing, press the Esc key

Data

The Data tab displays the JSON data that is used to create the JSON structure.

In the text panel, the data is displayed in read-only mode. You can select text for copy and paste operations.

You can query the data to be viewed. To query data, click .

Filter Settings

In the Filter Settings dialog box, you can specify filters on attributes for the data type: ARRAY, BOOLEAN, NUMBER and STRING.

You can set the filter settings for:

- Edit Filter Settings for:

- All

- Any

- Apply Filter Settings to:

- JSON Unnest: Applies filter to the JSON source data to be used for projection to relational data format. Only filtered data are projected.

- Aggregations: Applies filter to the JSON data to be used for aggregation only.

- JSON Unnest and Aggregations: Applies filter to both JSON unnest and data to be used for aggregation.

After you set the filter settings, click OK.

Additional Output

In the Additional Output tab, you can select relational columns in the source data for the output.

The input columns that are used by the aggregation definitions in the Aggregate tab are automatically added to the list for output.

You can perform the following tasks here:

- Add relational columns: Click to add relational columns in the Edit Output Data Column Dialog.

- Delete relational columns: Select the relational columns to delete and click .

- Edit Output Data Column Dialog
In the Edit Output Data Column dialog box, all the relational columns available in the data source are listed. You can select one or more columns to be added to the output.

Edit Output Data Column Dialog

In the Edit Output Data Column dialog box, all the relational columns available in the data source are listed. You can select one or more columns to be added to the output.

To add columns:

1. In the Available Attributes list, select the columns that you want to include in the output.

2. Click the right arrow to move the attributes to the Selected Attributes list. To exclude any columns from the output, select the attribute and click the left arrow.

3. Click OK. This includes the columns in the output, and they are listed in the Additional Output tab.

Related Topics

- Additional Output

Aggregate

You can define aggregation column definitions on JSON attributes in the Aggregate tab.

The Aggregate tab displays the information in two sections:

- Group By Attribute section: Here, the Group By attributes are listed, along with the attribute count. You can perform the following tasks:

- View JSON Paths: Click JSON Paths to display the attribute name with context information. For example, $."customers"."cust_id". If not enabled, then only the attribute name is displayed.

- Edit and Add Attributes: Click to add Group By attributes in the Edit Group By dialog box.

- Delete Attributes: Select the attributes to delete and click .

- Aggregate Attributes section: Here, the aggregation columns are displayed along with the column count.

- View JSON Paths: Click JSON Paths to display the attribute name with context information. For example, $."customers"."cust_id". If not enabled, then only the attribute name is displayed.

- Define Aggregations Columns: Click to define an aggregation column in the Add Aggregations dialog box.

- Delete Aggregation column: Click to delete selected columns.

- Add Aggregations
In the Add Aggregation dialog box, you can define functions for JSON attributes.

- Edit Sub Group By
In the Edit Sub Group By dialog box, you can add Sub Group By attributes to the selected JSON attribute.

- Edit Group By
The Edit Group By dialog box displays the relational columns above the JSON attribute collection.

Add Aggregations

In the Add Aggregation dialog box, you can define functions for JSON attributes.

The dialog box displays JSON structures in a hierarchical view. You can select multiple attributes and then apply an aggregation function to it.

Note:

Object and Array type attributes cannot be selected.

You can perform the following tasks:

- Define aggregation functions:

1. Select the JSON attributes. You can select multiple attributes by pressing the Ctrl key and clicking the attributes for which you want to define functions.

2. Click to select and apply a function for the selected attributes. The applicable functions are listed. Select the function that you want to apply.
Alternately, click the corresponding row in the Function column. The applicable functions are listed in a drop-down list. Select the function that you want to apply. Using this option you can define function for only one attribute at a time.

3. Click OK.

- Clear Aggregation Definition: Select the attribute and click . The defined function along with the output and Sub Group By entries are deleted.

- Edit Sub Group By Elements: Select the attribute and click . The Edit Sub Group By dialog box opens.

- Search: Click to find attribute based on partial attribute name.

Related Topics

- Edit Sub Group By

Edit Sub Group By

In the Edit Sub Group By dialog box, you can add Sub Group By attributes to the selected JSON attribute.

To add attributes:

1. In the upper pane, expand the Available Attributes folder.

2. Select the attributes that you want to add as Sub Group By attributes. The selected attributes are listed in the lower pane, which also displays the count of attributes that are added.

3. Click OK.

Edit Group By

The Edit Group By dialog box displays the relational columns above the JSON attribute collection.

You can add relational columns as part of the top level Group By. To add relational columns:

1. In the upper pane, expand the Available Attributes folder.

2. Select the columns that you want to add. The selected columns are listed in the lower pane.

3. Click OK.

Preview

You can preview the node output in the Preview tab.

The output is displayed in two tabs:

- Output Columns

- Output Data

Output Columns

In the Output Column tab, the columns in the header are displayed in a grid format. Click JSON Paths to view source attribute name.

- If you click JSON Paths, then the source attribute name along with the contextual information is displayed. For example, $."customers"."cust_id".

- If you do not click JSON Paths, then only the attribute name is displayed. For example, cust_id.

The following details of the columns are displayed in the Output Columns tab:

- Name: Displays the output column names

- Data Type: Displays the data type of the output column

- Data Source: Displays the source of the attribute name

- JSON Paths: Displays the attribute source

- Aggregate: Displays the aggregation function used for the aggregations

- Group By: Displays the Group By attributes

- Sub Group By: Displays the Sub-Group By attributes used in the aggregations

Output Data

In the Output Data tab, the query result of the top N rows is displayed. The query reflects the latest user specifications. The query result is displayed in a grid format.

JSON Query Node Properties

In the Properties pane, you can examine and change the characteristics or properties of a node.

To view the properties of a node, click the node and click Properties. If the Properties pane is closed, then go to View and click Properties. Alternately, right-click the node and click Go to Properties.

The JSON Query node Properties pane has these sections:

- Output
The Output section in the Properties pane displays the Output Columns in read-only mode.

- Cache
The Cache section provides the option to generate cache for the output data.

- Details
The Details section displays the name of the node and any comments about it.

Output

The Output section in the Properties pane displays the Output Columns in read-only mode.

Related Topics

- Output Columns

Cache

The Cache section provides the option to generate cache for the output data.

To generate cache output:

1. Select Generate Cache of Output Data to Optimize Viewing of Results to generate cache output.

2. In the Sampling Size field, select an option:

- Number of Rows: The default sampling size is 2000. Use the arrows to set a different number.

- Percent: Move the pointer to set the percentage.

Details

The Details section displays the name of the node and any comments about it.

You can change the name and add comments in the fields here:

- Node Name

- Node Comments

JSON Query Node Context Menu

The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

The context menu for a JSON Query Node has these selections:

- Connect

- Run

- Force Run

- Edit

- View Data

- Generate Apply Chain

- Show Event Log

- Validate Parents

- Deploy

- Save SQL

- Cut

- Copy

- Paste

- Select All

- Performance Settings. This opens the Edit Selected Node Settings dialog box, where you can set Parallel Settings and In-Memory settings for the node.

- Go to Properties

- Navigate

Related Topics

- JSON Query Node Editor

Data Types and their Supported Operators

Lists the data types and their supported operators for JSON data type.

Table 7-2 Data Types and their Supported Operators

Data Type

Supported Operators

Description

Array

In

Retrieves elements using specified indices, such as 0, 1, or index range such as 2:4, or a combination of both.

<

Retrieves elements that have indices less than the specified index.

Boolean

True

Retrieves elements that matches the condition.

False

Retrieves elements that do not match the condition.

Numbers

In

Retrieves numbers that are in the condition.

Not In

Retrieves numbers that are not included in the condition.

=

Retrieves numbers that are equal to the given condition.

!=

Retrieves numbers that are not equal to the given condition.

>

Retrieves numbers that are greater than the given condition.

>=

Retrieves numbers that are greater than or equal to the given condition.

<

Retrieves numbers that are lesser than the given condition.

<=

Retrieves numbers that are lesser than or equal to the given condition.

String

In

Retrieves elements that are in the condition.

Not In

Retrieves elements that are not in the condition.

Starts With

Retrieves elements that starts with the string in the condition.

Contains

Retrieves elements that contains elements that matches in the condition.

=

Retrieves elements that are equal to the condition.

!=

Retrieves elements that are not equal to the condition.

>

Retrieves elements that are greater than the condition.

>=

Retrieves elements that are greater than or equal to the condition.

<

Retrieves elements that are lesser than the condition.

<=

Retrieves elements that are lesser than or equal to the condition.

Sample

You can sample your data in the Sample tab.

A Sample node enables you to sample data in one of the following ways:

- Random Sample: A sample in which every element of the data set has an equal chance of being selected.

- Top N Sample: The default sample that selects the first N values.

- Stratified Sample: A sample is created as follows:

- First, the data set is divided into disjoint subsets or strata.

- Then, a random sample is taken from each subsets.

This technique is used when the distribution of target values is skewed greatly. For example, the response to a marketing campaign may have a positive target value of 1% of the time or less.

Sampling nested data is best done with a Case ID. The Sample node can run in parallel.

This section has the following topics:

- Sample Nested Data
Sampling nested data may require a case ID.

- Creating a Sample Node
You create a Sample node to create samples your data.

- Edit Sample Node
In the Edit Sample Node dialog box, you can define and edit a sample. The settings describe the type of sample to create and the size of the sample.

- Sample Node Properties
In the Properties pane, you can examine and change the characteristics or properties of a node.

- Sample Node Context Menu
The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

Sample Nested Data

Sampling nested data may require a case ID.

If you do not specify a case ID, then the sample operation may fail for a nested column that is very dense and deep. Failure may occur if the amount of nested data per row exceeds the maximum of 30,000 for a specific column or row.

A case ID also allows Data Miner to perform stratified sorts on data that is dense and deep.

Creating a Sample Node

You create a Sample node to create samples your data.

Before creating a Sample node, you must identify a Data Source node and the details of the sample

To create a Sample node:

1. In the Components pane, go to Workflow Editor. If the Components pane is not visible, then in the SQL Developer menu bar, go to View and click Components. Alternately, press Ctrl+Shift+P to dock the Components pane.

2. Expand the Transform section and click Sample.

3. Drag and drop the node from the Components pane to the Workflow pane.
The node is added to the workflow. The GUI shows that the node has no data associated with it. Therefore, it cannot be run.

4. Connect the Data Source node to the Sample node:

a. Move the cursor to the Data Source node.

b. Right-click the Data Source node and select Connect from the context menu.

c. Drag the line to the Sample node and click again.

5. Either double-click the Sample node, or right-click the Sample node, and click Edit. The Edit Sample Node dialog box opens.

6. Define the sample in the Edit Sample Node dialog box.

7. Right-click the Sample node and click Run. Monitor the running of the node in the Workflow Jobs. If the Workflow Jobs is not open, then go to View and click Data Miner. Under Data Miner, click Workflow Jobs.

8. After the running of the node is complete, right-click the Sample node and select View Data to view the results of the sample.

Related Topics

- Edit Sample Node

Edit Sample Node

In the Edit Sample Node dialog box, you can define and edit a sample. The settings describe the type of sample to create and the size of the sample.

To edit the settings for the Sample node:

1. Open the Edit Sample Node dialog box:

- Either double-click the Sample node, or right-click the Sample node, and select Edit.

- Select the node and go to the Settings tab in the Sample node Properties pane.

2. In the Edit Sample Node dialog box, you can provide and edit the following details:

- Sample Size: This is the number of rows in the sample. You can specify the number of rows in terms of:

- Number of rows (default)

- Percent The default is 60 percent.

- Rows: This is the number of rows in the sample. You can change the default value, and enter a different value. The default is 2000.

- Sample Type: The options are:

- Random (Default)

- Top N

- Stratified

- Random

- Top N

- Stratified

- Custom Balance
The Custom Balance dialog box enables you to specify exactly how the selected column is balanced.

Random

For a random sample, specify the following:

- Seed: The default seed is 2345 You can specify a different integer.

- Case ID (optional): Select a case ID from the drop-down list.

If you specify a seed and a case ID, then the sample is reproducible.

Top N

No other specifications are available for Top N.

Stratified

For a stratified sample, specify the following:

- Column: Select the column for stratification.

- Seed: Default seed=12345 You can specify a different integer.

- Case ID (optional): Select a case ID from the drop-down list.
If you specify a seed and a case ID, then the sample is reproducible.

- Distribution: Specify how the sample is to be created. There are three options:

- Original: The distribution of the selected column in the sample is the same as the distribution in data source. For example, if the column GENDER has M as the value for 95 percent of the cases, then in the sample, the value of GENDER is M for 95% of the cases.

- Balanced: The distribution of the values of the column is equal in the sample, regardless of the distribution in the data source. If the column is GENDER, and GENDER has two values M and F, then 50% of the time the value of GENDER is M.

- Custom: You define how the values of the columns are distributed in the sample. You must run the node once before you define the custom distribution. Click Edit to open the Custom Balance dialog box.

The Stratified dialog box displays a histogram of the values of the selected column at the bottom of the window. To see more details, click View to display the Custom Balance dialog box.

Related Topics

- Custom Balance

Custom Balance

The Custom Balance dialog box enables you to specify exactly how the selected column is balanced.

You must run the node to collect statistics before you create custom balance. After you run the node, edit it, select Custom Distribution and click View. The Custom Balance dialog opens:

You can either create custom entries for each value of the stratify attribute, or you can click Original or Balanced to provide a starting point. Click Reset to reset to the original values.

To create a custom value, select the attribute to change and click .

Change the value in the Sample Count column to the custom value. Press Enter. The new sample is displayed as output in the lower part of the screen. Change as many values as required. Click OK when you have finished.

Sample Node Properties

In the Properties pane, you can examine and change the characteristics or properties of a node.

To view the properties of a node, click the node and click Properties. If the Properties pane is closed, then go to View and click Properties. Alternately, right-click the node and click Go to Properties.

The Sample node Properties pane has these sections:

- Settings: You can specify the following:

- Sample Size: Select a sample size in terms of:

- Percent. Default=60%

- Number of Rows. The default number of rows is 2000.

- Sample Type: The options are:

- Random (Default)

- Stratified

- Top N

- Seed: The default seed is 12345. You can specify a different integer.

- Case ID: This is an optional field. Select a case ID from the drop-down list. If you specify a seed and a case ID, then the sample is reproducible.

- Cache

- Details

- Cache
The Cache section provides the option to generate a cache for output data. You can change this default using the transform preference.

- Details
The Details section displays the name of the node and any comments about it.

Related Topics

- Random

- Stratified

- Top N

Cache

The Cache section provides the option to generate a cache for output data. You can change this default using the transform preference.

You can perform the following tasks:

- Generate Cache of Output Data to Optimize Viewing of Results: Select this option to generate a cache. The default setting is to not generate a cache.

- Sampling Size: You can select caching or override the default settings. Default sampling size is Number of Rows Default Value=2000

Related Topics

- Transforms

Details

The Details section displays the name of the node and any comments about it.

You can change the name and comments in the fields here:

- Node Name

- Node Comments

Sample Node Context Menu

The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

Right click the Sample node. The following options are available in the context menu:

- Connect

- Edit

- Validate Parents

- Run

- View Data

- Force Run

- Deploy

- Show Graph

- Generate Apply Chain

- Cut

- Copy

- Extended Paste

- Paste

- Performance Settings. This opens the Edit Selected Node Settings dialog box, where you can set Parallel Settings and In-Memory settings for the node.

- Select All

- Show Event Log

- Show Runtime Errors. Displayed if there are errors.

- Show Validation Errors. Displayed if there are validation errors.

- Navigate

Related Topics

- Data Viewer

Transform

A Transform node can use either sampled data or all data to calculate statistics.

You define transformation on a per-column basis. After you have defined a transformation, you can transform several columns in the same way. The Transform node can run in parallel. To use a Transform node, connect it to a data flow, that is a Data Source node or some other node such as a filtering node that produces attributes. Then select the attributes to transform.

- Supported Transformations
The transformations available depend on the data type of the attribute. For example, normalization cannot be performed on character data.

- Support for Date and Time Data Types
Lists the supported data types for Transform nodes.

- Creating Transform Node
You create a Transform node to define transformation and transform columns.

- Edit Transform Node
You can define and edit the Transform node using the Edit Transform Node dialog box.

- Transform Node Properties
In the Properties pane, you can examine and change the characteristics or properties of a node.

- Transform Node Context Menu
The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

Supported Transformations

The transformations available depend on the data type of the attribute. For example, normalization cannot be performed on character data.

You define transformation on a per-column basis. After you have defined a transformation, you can transform several columns in the same way.

You can use these statistics as a guide to defining one of several transformation. The following transformations are supported:

- Binning
Binning is a transformation type that converts continuous variable into a categorical variable, continuous value into a continuous value, and categorical value into a categorical variable.

- Custom
In the Custom dialog box, you can compute a new value for a field based on combinations of existing attributes and common function.

- Missing Values
The Missing Values Transform enables you to specify how missing values are treated.

- Normalization
Normalization consists of transforming numeric values into a specific range, such as [–1.0,1.0] or [0.0,1.0] such that x_new = (x_old-shift)/scale. Normalization applies only to numeric attributes.

- Outlier
An Outlier is a data value that is not in the typical population of data, that is, extreme values. In a normal distribution, outliers are typically at least 3 standard deviations from the mean.

Related Topics

- About Parallel Processing

- About Oracle Database In-Memory

Binning

Binning is a transformation type that converts continuous variable into a categorical variable, continuous value into a continuous value, and categorical value into a categorical variable.

Binning converts the following:

- A continuous variable into a categorical variable.

- A continuous value into a continuous value. For example, age can be converted to 10 groups from 1 to 10.

- A categorical value with many values into a categorical variable with fewer variables.

For example, salary is a continuous variable. If you divide salary into 10 bins, then you convert salary into a categorical variable with 10 values, where each value represents a salary range.

You can bin both numbers and character types VARCHAR2 and CHAR.

- Recode

Recode

Oracle Data Miner does not support recode transformations. However, you can use custom binning to perform a recode transformation. For example, to recode the US states ME, NH, VT, CT, MA, and RI to the value NE, create a custom bin that puts the 5 states into a bin named NE.

Custom

In the Custom dialog box, you can compute a new value for a field based on combinations of existing attributes and common function.

Use Expression Builder to create the new attribute.

Related Topics

- Expression Builder

Missing Values

The Missing Values Transform enables you to specify how missing values are treated.

A data value can be missing for a variety of reasons:

- If the data value was not measured, that is, it has a Null value.

- If the data value was not answered.

- If the data value was unknown.

- If the data value was lost.

Data Mining algorithms vary in the way they treat missing values:

- Ignore the missing values, and then omit any records that contain missing values.

- Replace missing values with the mode or mean.

- Infer missing values from existing values.

Normalization

Normalization consists of transforming numeric values into a specific range, such as [–1.0,1.0] or [0.0,1.0] such that x_new = (x_old-shift)/scale. Normalization applies only to numeric attributes.

Oracle Data Miner enables you to specify the following kinds of normalization:

- Min Max:: Normalizes each attribute using the transformation x_new = (x_old-min)/(max-min)

- Linear Scale: Normalizes each attribute using the transformation x_new = (x_old-shift)/scale

- Z-Score: Normalizes numeric attributes using the mean and standard deviation computed from the data. Normalizes each attribute using the transformation x_new = (x-mean)/standard deviation

- Custom: The user defines normalization.

Normalization provides transformations that perform min-max normalization, scale normalization, and z-score normalization.

Note:

You cannot normalize character data.

Outlier

An Outlier is a data value that is not in the typical population of data, that is, extreme values. In a normal distribution, outliers are typically at least 3 standard deviations from the mean.

You specify a treatment by defining what constitutes an outlier (for example, all values in the top and bottom 5 percent of values) and how to replace outliers.

Note:

Usually, you can replace outliers with null or edge values.

For example:

Mean of an attribute distribution=10

Standard deviation=5

Outliers are values that are:

- Less than -5 (The mean minus 3 times the standard deviation)

- Greater than 25 (The mean plus three times the standard deviation)

Then, in this case you can either replace the outlier -10 with Null or with 5.

Support for Date and Time Data Types

Lists the supported data types for Transform nodes.

The Transform Node provides limited support for these data types for date and time:

- DATE

- TIMESTAMP

- TIMESTAMP_WITH_TIMEZONE

- TIMESTAMP_WITH_LOCAL_TIMEZONE

You can bin date and time attributes using Equal Width or Custom binning. You can apply Statistic and Missing Value transformation with either Statistic or Value treatment.

Creating Transform Node

You create a Transform node to define transformation and transform columns.

Before you specify a transform, you must identify a Data Source node or any other node which provides data, such as the Create Table Node, and the details of the transform.

To create a Transform node:

1. In the Components pane, go to Workflow Editor. If the Components pane is not visible, then in the SQL Developer menu bar, go to View and click Components. Alternately, press Ctrl+Shift+P to dock the Components pane.

2. Expand the Transform section and click Transform.

3. Drag and drop the node from the Components pane to the Workflow pane.
The node is added to the workflow. The GUI shows that the node has no data associated with it. Therefore, it cannot be run.

4. Connect the Data Source node to the Transform node:

a. Move the cursor to the Data Source node.

b. Right-click the Data Source node and select Connect.

c. Drag the line to the Transform node and click again.

5. Either double-click the Transform node or right-click it node and select Edit. Use the Edit Transform Node dialog box to define the transform.

6. Right-click the Transform node and select Run. Monitor the running of the node in Workflow Jobs. If Workflow Jobs is not open, then go to View and click Data Miner. Under Data Miner, click Workflow Jobs.

7. After the running of the node is complete, right-click the Transform node and select View Data to view the results of the transform.

Related Topics

- Edit Transform Node

Edit Transform Node

You can define and edit the Transform node using the Edit Transform Node dialog box.

This dialog box has two tabs:

- Transformations

- Statistics

In the Transformation tab, the statistics for each column is displayed. If you do not want to see statistics, then deselect Show Statistics.

Note:

You must run the node to see statistics.

You can perform the following tasks in the Transformations tab:

- Define Transformation: Select one or more original columns, that is, columns that are not transformed. Click .
The Add Transform dialog box opens if you selected one or fewer columns. Otherwise, the Apply Transform Wizard opens.

- Define Custom Transformation: Select one or more original columns, that is, columns that are not transformed. Click .
The Add Custom Transform dialog box opens. You can add custom transformations here.

The default behavior is to ignore the original column and to use the transformed column as output. The values displayed in the Output column are indicated by:

- : For a column that is included

- : For a column that is ignored

- Change Values in the Output Column: Click the icon displayed in the Output column to edit the values in the Add Transform dialog box.

- Edit Transformed Column: You can edit transformed columns only. For example, you can edit AGE_BIN, but not AGE. To edit transforms, select one or more transformed columns and click . The Edit Transform dialog box opens if you selected one or fewer columns.

- Delete Transform: Select one or more transformed columns and click .

- Filter Column: To limit the number of columns displayed, click . You can search by:

- Output Column

- Transform

- Source Column

- Clear filter definition: To clear filter definitions, click .

- View effects of transform: To view the effect of a transformation:

- Run the node.

- After running of the node is complete, double-click the node.

- Select the transformed column to see histograms that compare the original column with the transformed column.

When a column has a transformation applied to it, a new row in the list of columns is generated. Because each column must have a name, the name of the new row is based on the name of the old column and the type of transformation performed. Typically users want to transform a column and only have the output of the transform node contained in the new column. The original column has an option set to prevent it from being passed as one of the output columns. For example, if you bin AGE creating AGE_BIN, then AGE is not passed on and AGE_BIN is passed on.

- Add Transform
The Add Transform dialog box allows you to add custom transformation such as Binning, Missing Values, Normalization and so on, depending on the attribute of the data type.

- Add Custom Transform
In the Add Custom Transform dialog box, you can define a custom transformation.

- Apply Transform Wizard
The Apply Transform wizard enables you to define or edit transformations for several columns at the same time.

- Edit Transform
If the node has run, then the Edit Transform dialog box displays information about both the untransformed column and transformed version

- Edit Custom Transform
In the Edit Custom Transform dialog box, you can edit expressions using the Expression Builder.

Add Transform

The Add Transform dialog box allows you to add custom transformation such as Binning, Missing Values, Normalization and so on, depending on the attribute of the data type.

To add a transformation:

1. In the Edit Transform Node dialog box, click . The Add Transform dialog box opens. To add a custom transformation, click .

2. In the Transform Type field, select a transform type, that is, the type of transformation that you want to define. Default type is Binning. The field in the Add Transform dialog box depend on the transform type that you select:

- Binning:. For binning transformation, enter the details as applicable.

- Missing Values

- Normalization

- Outlier

- Use Existing Column

3. After you are done, click OK.

- Binning
Binning is a type of transformation.

- Bin Equal Width (Number)
The Bin Equal Width selection determines bins for numeric attributes by dividing the range of values into a specified number of bins of equal size.

- Bin Quantile
The Bin Quantile selection divides attributes into bins so that each bin contains approximately the same number of cases.

- Bin Top N
The Bin Top N type bins categorical attributes. The bin definition for each attribute is computed based on the occurrence frequency of values that are computed from the data.

- Custom
Custom binning enables you to define custom bins.

- Missing Values
Missing Values is a transformation type that replaces missing values with an appropriate value.

- Normalization
Normalization consists of transforming numeric values into a specific range, such as [–1.0,1.0] or [0.0,1.0] so that x_new = (x_old-shift)/scale.

- Outlier
An outlier is a data value that does not come from the typical population of data. In other words, it is an extreme value.

- Use Existing Column
The Use Existing Column option is available only when there is at least one transformation.

- Add or Edit Multiple Transforms
You can define or edit transformations for several column at a time. You can also apply an existing transformation to one or more columns.

Binning

Binning is a type of transformation.

You can use Binning to:

- Transform a continuous variable to a discrete one.

- Transform a variable with large number of discrete values to one with a smaller number of discrete values.

The default transformation type is Binning. The types of binning supported depend on the data type of the column:

- For numeric data type NUMBER, the supported types of binning are:

- Bin Equal Width (Number) (Default)

- Bin Quantile

- Custom

- For categorical data type VARCHAR2, the supported types of binning are:

- Bin Top N (Default)

- Custom

- For date and time data types DATE, TIMESTAMP, TIMESTAMP WITH LOCAL TIMEZONE, and TIMESTAMP WITH TIMEZONE, the supported types of binning are:

- Bin Equal Width (Number) (Default)

- Custom

Note:

The number of bins must be two.

Bin Equal Width (Number)

The Bin Equal Width selection determines bins for numeric attributes by dividing the range of values into a specified number of bins of equal size.

Edit the following fields:

- Bin Count: You can change the Bin Count to any number greater than or equal to 2. The default count is set to 10.

- Bin Label: Select a different Bin Label scheme from the list. The default is set to Range.

Click OK when you have finished.

Bin Quantile

The Bin Quantile selection divides attributes into bins so that each bin contains approximately the same number of cases.

Edit the following fields:

- Bin Count: You can change the Bin Count to any number greater than or equal to 2. The default count is set to 10.

- Bin Label: You can select a different Bin Label scheme from the list. The default is set to Range.

Click OK when you have finished.

Bin Top N

The Bin Top N type bins categorical attributes. The bin definition for each attribute is computed based on the occurrence frequency of values that are computed from the data.

Specify N, the number of bins. Each of the bins bin_1, …, bin_N contains the values with the highest frequencies. The last bin_N contains all remaining values.

You can change the Bin Count to any number greater than or equal to 3. The default count is set to 10.

Click OK when you have finished.

Custom

Custom binning enables you to define custom bins.

To define bins, click Bin Assignment and then modify the default bins. After you generate default bins, you can modify the generated bins in several ways:

- Edit Bin Name: If it is a Range label.

- Delete Bins: Select it and click .

- Add Bins: Click .

- Edit Bins: Select a bin and click .

- Bin Assignment
In the Bin Assignment dialog box, you can specify options for bin type, bin count, bin labels and so on for different data types.

- Edit Bin
The data type of the attribute determines the way to edit bins.

- Add Bin
You can add bins for Categorical and Numerical data types.

Bin Assignment

In the Bin Assignment dialog box, you can specify options for bin type, bin count, bin labels and so on for different data types.

To assign bins, select the following options:

- Binning Type: The default type depends on the data type of the attribute that you are binning:

- If the data type of the attribute is Number, then the default binning type is Bin Equal Width.

- If the data type of the attribute is Character, then the default binning type is Bin Top N.

You can change the binning type for numbers.

- Bin Count: The default count is 10. You can change this to any integer greater than 2.

- Bin Labels: The default label for numbers is Range. You can change the bin label to Number.

- Transform NULLs: If the Transform NULLs check-box is selected for a binning transformation that produces NUMBER data type, then null values are placed into the last bin. For example, if the AGE column has null values and Equal Width Binning was requested with Bin Labels value equal to Number, and the number of bins is 10, then null values will be in bin number 11. For this option, the following conditions apply:

- When deselected, null values are excluded from the generated transformation SQL.

Note:

Applicable only for those binning transformations that produce VARCHAR2 data type after transformation.

- This field is not editable for those binning transformations which produce numeric data type after transformation.

- For legacy workflows, this field is selected by default, and the corresponding field contains the value Null bin.

Click OK when you have finished. You return to the Custom display where you modify the generated bins.

Edit Bin

The data type of the attribute determines the way to edit bins.

You can edit bins for number and characters in the following ways:

- For numbers: Edit lower bounds in the grid. You cannot edit a bin without a lower bound. You cannot add a value that is less than the prior bin lower bound value or greater than the following bin lower bound value.

- For characters: The Edit Custom Categorical Bins dialog box has two columns:

- Bins: You can add bins, delete a selected bin and change the name of the selected bin.

- Bin Assignment: You can delete values for the selected Bin.

Click OK when you have finished editing bins. If you are editing custom bins for categorical, then first click OK twice, once to closed the Edit Custom Categorical Bins dialog box.

Add Bin

You can add bins for Categorical and Numerical data types.

To add bins:

- Categoricals: Open the Edit Custom Categorical Bins and click . The new bin has a default name that you can change. Add values to the bin in the Bin Assignment column.

- Numericals: Select a bin and click . You can change the name of the bin and add a range of values.

Missing Values

Missing Values is a transformation type that replaces missing values with an appropriate value.

To specify a Missing Values transformation:

1. In the Transform Type field, select the option Missing Values.

2. In the Missing Values field, select an option:

- Statistic: Replaces the missing value with a statistical measure. Statistic is the default treatment for missing values. The applicable Statistic type depends on the data type of the column:

- For numeric columns, you can replace missing values with Mean (default), Median, Minimum, Maximum.

- For categorical columns, you can replace missing values with Mode (default).

- Value: Replaces Missing Values with the specified value. Oracle Data Miner provides a default value that you can change.

- If statistics are not available, then the default value is 0.

- If statistics are available, then the default value is: Mean for numerical columns Mode for categorical columns

Both these treatments can be applied to attributes that have a date or time data type DATE, TIMESTAMP, TIMESTAMP_WITH_LOCAL_ TIMEZONE, and TIMESTAMP_WITH_TIMEZONE.

3. After you are done, click OK.

Normalization

Normalization consists of transforming numeric values into a specific range, such as [–1.0,1.0] or [0.0,1.0] so that x_new = (x_old-shift)/scale.

Normalization usually results in values whose absolute value is less than or equal to 1.0.

Note:

Normalization applies only to numeric columns. Therefore, you can normalize numeric attributes only.

To normalize a column:

1. In the Transform Type field, select the option Normalization.

2. In the Normalization Type field, select a type from the drop-down list. Oracle Data Miner supports these types of normalization:

- Min Max: Normalizes the column using the transformation x_new = (x_old-min)/ (max-min). The default is min-max.

- Z-score: Normalizes numeric columns using the mean and standard deviation computed from the data. Normalizes each column using the transformation x_new = (x-mean)/standard deviation.

- Linear Scale: Normalizes each column using the transformation x_new = (x-0)/ max(abs(max), abs(min)).

- Manual: Defines normalization by specifying the shift and scale for the transformation x_new = (x_old-shift)/scale. If you select Manual, then specify the following:

- Shift

- Scale

3. After you are done, click OK.

Outlier

An outlier is a data value that does not come from the typical population of data. In other words, it is an extreme value.

In a normal distribution, outliers are typically at least 3 standard deviations from the mean. Outliers are usually replaced with values that are not extreme, or they are replaced with Null.

Note:

You can define outlier treatments for numeric columns only.

To define an Outlier transformation:

1. In the Transform Type field, select the option Outlier.

2. In the Outlier Type field, select any one of the following options:

- Standard Deviation: This is the default Outlier type. For this outlier type, enter a Standard Deviation to define the Outlier in the following field:

- Multiples of Sigma: This is the number of standard deviations that define an outlier. The default is 3, that is, 3 standard deviations. 3 Standard Deviation means that an outlier is a value less than mean - 3 * Standard Deviation or greater than mean + 3* Standard Deviation.

- Percent: Enables you to specify that outliers are values in a bottom percentage and a top percent. The default is to specify that outliers are in the bottom 5 percent or in the top 5 percent. You can change the defaults by entering values in these fields:

- Lower Percent Value

- Upper Percent Value

- Value: Enables you to specify a lower value and an upper value so that outliers are those values less than the lower value or greater than the upper value. You can change these values, but the upper value must be bigger than the lower value.

- Lower Value: If statistics are available, then the default is -3* standard deviation. If statistics are not available, then the default is 0.

- Upper Value: If statistics are available, then the default is +3* standard deviation. If statistics are not available, then the default is 1.

3. In the Replace With field, select an option to specify how to replace outliers. The options are:

- Null (Default)

- Edge Value
For example: If the mean of a column distribution is 10, and If standard deviation is 10,then the outliers can be:

- Values that are less than -5, that is, Mean-3*Standard Deviation

- Values that are greater than 25, that is, Mean+3*Standard Deviation

Outlier=-10. You can replace -10 with Null or with -5, which is the edge value.

4. After you are done, click OK.

Use Existing Column

The Use Existing Column option is available only when there is at least one transformation.

This selection is used when you add, or edit multiple transformation.

Related Topics

- Add or Edit Multiple Transforms

Add or Edit Multiple Transforms

You can define or edit transformations for several column at a time. You can also apply an existing transformation to one or more columns.

To add or edit transformations for multiple transforms:

1. Double-click the Transform node. The Transformation Editor opens.

2. To define the same transform for several columns, select the columns. You can select columns with different but compatible data types. For example, CHAR and VARCHAR are characters, and are compatible data types. If there are no transformations that apply to all the columns, then you get a message. Click .
The Apply Transform Wizard opens.

a. Select the Transform type to apply to all columns.

b. Provide the specific details related to the transform type that you have selected.

c. Click Next.

d. Click Generate Statistic.

e. Click Finish.

3. If you have already transformed a column, then you can define the same transformation for several other columns.
Suppose you have binned AGE creating AGE_BIN. To bin several columns in the same way, select AGE and the columns that you want to bin in the same way. Click .

The Apply Transform Wizard opens.

a. For Transform Type, select Use Existing AGE_BIN is listed as the Transformed column. You cannot change any other values.

b. Click Next. You can change the names of the output columns.

c. Select Generate Statistic on Finish.

d. Click Finish.

4. To edit several transformations at the same time, select the transformations and click .
The Apply Transform Wizard opens. Edit the transformation and click Finish.

Related Topics

- Apply Transform Wizard

Add Custom Transform

In the Add Custom Transform dialog box, you can define a custom transformation.

The default name for the new attribute is EXPRESSION. You can change this name.

In the Add Custom Transform dialog box, you can perform the following tasks:

- Add an expression: Click . The Expression Builder opens. Use expression build to define an expression.

- Validate the expression.

- Click OK.

- Edit a custom transformation.

- Delete a custom Transformation: Click .

Related Topics

- Edit Custom Transform

- Expression Builder

Apply Transform Wizard

The Apply Transform wizard enables you to define or edit transformations for several columns at the same time.

The first step of the wizard is similar to the Add Transform dialog box. You cannot select custom transformations.

1. In the Choose Transformation section:

- Transform: Select the transform type.

- Provide the details related to the selected transformation type.

2. Click Next.

3. In the Choose Columns section, specify names for the transformed columns. You can accept the names or change them. The available transformations are those transformations that can be performed on all selected columns. This is an optional section.

4. Click Finish.

- Define Columns
The second step of the Apply Transform wizard enables you to specify names for the transformed columns. You can accept the names or change them.

Related Topics

- Add Transform

Define Columns

The second step of the Apply Transform wizard enables you to specify names for the transformed columns. You can accept the names or change them.

The default is not to generate statistics on finish. Select the check box to generate statistics.

Click Finish when you have finished.

Edit Transform

If the node has run, then the Edit Transform dialog box displays information about both the untransformed column and transformed version

The Edit Transform dialog box is similar to the Add Transform dialog box.

The Edit dialog box displays information about both the untransformed column and transformed version in the following tabs:

- The Histogram tab shows histogram for both the untransformed attribute and the transformed in two sets of histograms. On the left side of the tab are histograms for the untransformed column. On the right side of the tab are histograms for the transformed column.

- The Statistics tab shows statistics for the transformed data and for the original data.

Note:

When you transform data, the transformed data may have a different data type from the type of the original data. For example, AGE has type NUMBER and AGE_BIN has type VARCHAR2.

Related Topics

- Add Transform

Edit Custom Transform

In the Edit Custom Transform dialog box, you can edit expressions using the Expression Builder.

To edit an expression:

1. Select the attribute and click . The Expression Builder opens.

2. Use the Expression Builder to modify the expression.

3. Validate the expression.

4. Click OK.

To delete an expression, click .

Related Topics

- Expression Builder

Transform Node Properties

In the Properties pane, you can examine and change the characteristics or properties of a node.

To view the properties of a node, click the node and click Properties. If the Properties pane is closed, then go to View and click Properties. Alternately, right-click the node and click Go to Properties.

The Transform node Properties pane has these sections:

- Transform: Specifies how the transformations are defined. You can modify these values.
The transformations are summarized in a grid. For each column name (data) type, transform, and output are displayed. If you bin AGE creating AGE_BIN, then AGE is not used as output, that is, is not passed to subsequent nodes.

- Histogram: Specifies the number of bins used in histograms. You can specify a different number of bins for histograms created for numeric, categorical, and date data types. The default is 10 bins for all data types.

- Sample

- Cache

- Details

Related Topics

- Edit Transform Node

Transform Node Context Menu

The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

Right click the Transform node. The following options are available in the context menu:

- Connect

- Edit

- Validate Parents

- Run

- View Data

- Force Run

- Deploy

- Show Graph

- Generate Apply Chain

- Cut

- Copy

- Extended Paste

- Paste

- Select All

- Performance Settings. This opens the Edit Selected Node Settings dialog box, where you can set Parallel Settings and In-Memory settings for the node.

- Show Event Log

- Show Runtime Errors. Displayed if there are errors.

- Show Validation Errors. Displayed if there are validation errors.

- Navigate

Related Topics

- Data Viewer

8 Model Nodes

Model nodes specify the models to build and the models to add to the workflow.

The Models section in the Components pane contains the Models nodes. The models in the Components pane are:

- Types of Models
Lists the types of Model nodes supported by Oracle Data Miner.

- Automatic Data Preparation (ADP)
Automatic Data Preparation (ADP) transforms the build data according to the requirements of the algorithm, embeds the transformation instructions in the model, and uses the instructions to transform the test or scoring data when the model is applied.

- Data Used for Model Building
Oracle Data Miner does not necessarily use all the columns in a Data Source when it builds models.

- Model Nodes Properties
The Properties of the Model node allows you to examine and change characteristics of the node.

- Anomaly Detection Node
An Anomaly Detection node builds one or more models that detect rare occurrences, such as fraud, using the One-Class SVM algorithm.

- Association Node
The Association node defines one or more Association models. To specify data for the build, connect a Data Source node to the Association node.

- Classification Node
The Classification node defines one or more classification models to build and to test.

- Clustering Node
A Clustering node builds clustering models using the k-Means, O-Cluster, and Expectation Maximization algorithms.

- Explicit Feature Extraction Node
The Explicit Feature Extraction node is built using the feature extraction algorithm called Explicit Semantic Analysis (ESA).

- Feature Extraction Node
A Feature Extraction node uses the Nonnegative Matrix Factorization (NMF) algorithm, to build models.

- Model Node
Model nodes rely on database resources for their definition. It may be necessary to refresh a node definition if the database resources change.

- Model Details Node
The Model Detail node extracts and provides information about the model and algorithms.

- R Build Node
The R Build Node allows you to register R models. It builds R models and generates R model test results for Classification and Regression mining function. R Build nodes supports Classification, Regression, Clustering, and Feature Extraction mining functions only.

- Regression Node
The Regression node defines one or more Regression models to build and to test.

- Advanced Settings Overview
The Advanced Settings dialog box enables you to edit data usage and other model specifications, add and remove models from the node.

- Mining Functions
Mining functions represent a class of mining problems that can be solved using data mining algorithms.

Types of Models

Lists the types of Model nodes supported by Oracle Data Miner.

The types of models available are:

- Anomaly Detection Node: Builds Anomaly Detection models using a one-class Support Vector Machine (SVM).

- Association Node: Builds models for market basket analysis.

- Classification Node: Builds and tests classification models with the same target, case ID, cost, and split settings, where relevant. The models use the classification algorithms: Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT), and Generalized Linear Model (GLM).

- Clustering Node: Builds clustering models using the clustering algorithms: k-Means, O-Cluster, and Expectation Maximization (EM). EM requires Oracle Database 12c Release 1 (12.1) or later.

- Explicit Feature Extraction Node: Builds feature extraction models using the Explicit Semantic Analysis algorithm.

- Feature Extraction Node: Builds feature extraction models using the feature extraction algorithms: nonnegative matrix factorization, principal components analysis (PCA), and singular value decomposition (SVD). PCA and SVD require Oracle Database 12c Release 1 (12.1) or later.

- Model Node: Adds models to a workflow that were not built in the current workflow. This node has no input data.

- Model Details Node: Extracts model details from a model build node, a Model node, or any node that produces a model.

- Regression Node: Builds and tests a collection of Regression models with the same target, case ID, cost, and split settings, where relevant. The models use the regression algorithms: SVM and GLM.

Automatic Data Preparation (ADP)

Automatic Data Preparation (ADP) transforms the build data according to the requirements of the algorithm, embeds the transformation instructions in the model, and uses the instructions to transform the test or scoring data when the model is applied.

Data used for building a model must be properly prepared. Different algorithms have different input requirements. For example, Naive Bayes requires binned data.

If you are connected to Oracle Database 12c or later, then ADP prepares text data.

- Numerical Data Preparation
Automatic Data Preparation prepares numerical data for different algorithms in different ways.

- Manual Data Preparation
For manual data preparation, you must understand the requirements of each algorithm and carry out the transformations in order to prepare the test data or scoring data.

Numerical Data Preparation

Automatic Data Preparation prepares numerical data for different algorithms in different ways.

Here are some examples of how ADP prepares numerical data:

- For algorithms that require binned data (such as Naive Bayes), ADP performs supervised binning. Supervised binning is a special binning approach that takes into account the target to find good cut-points in the predictor.

- For algorithms that require normalized data (such as Support Vector Machines), the numerical data is normalized.

- For algorithms that can handle untransformed data (such as Decision Tree), you can use the numerical data to find splitters in the tree with an approach similar to supervised binning.

Manual Data Preparation

For manual data preparation, you must understand the requirements of each algorithm and carry out the transformations in order to prepare the test data or scoring data.

You must perform manual binning for data which has business meaning, such as recoding a numeric column of ages to desired ranges like YOUTH, ADULT and so on. Otherwise, automatic data preparation is recommended.

Data Used for Model Building

Oracle Data Miner does not necessarily use all the columns in a Data Source when it builds models.

Model nodes use a set of heuristics to determine whether to exclude columns from the model building process or change the mining type from numerical to categorical only.

- There are several reasons for not using a particular column for model building. If a column does not contain useful information, then it is usually not used.
The exact list of attributes used as input to build the model depends on the algorithm used to build the model. If an algorithm does not support a data type, then Oracle Data Miner does not use attributes with that data type as input.

For models that have targets, such as Classification models, the target cannot be text.

- The same mining types are used for all models.

If you are connected to Oracle Database 12c Release 1 (12.1) and later, then specify the characteristics of Text attributes when you edit the Build node.

- Viewing and Changing Data Usage
You can view and change data usage in the Input tab of the Build Editor and in the Advanced Settings dialog box.

- Text
Text is available for any of the following data types: CHAR, VARCHAR2, BLOB, CLOB, NCHAR, or NVARCHAR2.

Viewing and Changing Data Usage

You can view and change data usage in the Input tab of the Build Editor and in the Advanced Settings dialog box.

- Input Tab of Build Editor
In the Input tab, the setting Determine inputs automatically (using heuristics) controls the automatic selection of attributes to be used as inputs, and the automatic selection of mining types.

- Advanced Settings
In the Advanced Settings dialog box, you can edit settings related to model settings, data usage, performance settings, and algorithm settings.

Input Tab of Build Editor

In the Input tab, the setting Determine inputs automatically (using heuristics) controls the automatic selection of attributes to be used as inputs, and the automatic selection of mining types.

To edit a Build node:

1. Double-click the node or right-click the node and select Edit.

2. Click the Input tab. In the Input tab, the field Determine inputs automatically (using heuristics) is selected by default for all models. Oracle Data Miner determines which attributes to use for input and characteristics of the attributes. Oracle Data Miner also determines the mining type, and specifies that auto data preparation is performed for all attributes. After the model is run, Oracle Data Miner generates rules describing the changes that it made, such as excluding an attribute or changing the mining type. To see detailed information about the heuristics, click Show.

Note:

You cannot view and edit data usage for an Association model using these steps.

- Automatic Input
When Automatic Input is selected, Oracle Data Miner does not use attributes that do not provide useful information. For example, attributes that are almost constant may not be suitable for input.

- Manual Input
To specify inputs manually, deselect Determine Inputs Automatically (using heuristics).

Automatic Input

When Automatic Input is selected, Oracle Data Miner does not use attributes that do not provide useful information. For example, attributes that are almost constant may not be suitable for input.

After the node runs, rules describe the heuristics used. Click Show to see detailed informations.

Manual Input

To specify inputs manually, deselect Determine Inputs Automatically (using heuristics).

You can make the following changes by using the Manual Input option:

- To ignore an attribute: If you do not want to use an attribute as input, then go to the Input column and click the output icon . Select the ignore icon and click OK. The attribute will not be used. It will be ignored. Similarly, to use an attribute that you have ignored, click in the Input column and select . The attribute is used in model build.

- To change mining type of an attribute: Go to the Mining Type column and select an option from the drop-down list:

- Numerical

- Categorical

Text mining types are Text and Text Custom. Select Text Custom to create a column- level text specification.

- To manually prepare data: By default, Automatic Data Preparation (ADP) is performed on all attributes. If you do not want Automatic Data Preparation performed for an attribute, then deselect the corresponding check box for that attribute in the Auto Prep column. If you turn off Auto Prep, then you are responsible for data preparation for that attribute.

Note:

If the mining type of an attribute is Text or Text Custom, then you cannot deselect Automatic Data Preparation.

Related Topics

- Automatic Data Preparation (ADP)

Advanced Settings

In the Advanced Settings dialog box, you can edit settings related to model settings, data usage, performance settings, and algorithm settings.

To view which columns are selected by Oracle Data Miner and what mining type is assigned to each selected column, follow these steps:

Note:

You cannot view and edit data usage for an Association Model using these steps.

1. Connect the Data Source node to the Model node.

2. Right-click the Model node and select Run.

3. Open the Advanced Settings dialog box in one of these ways:

- After the model build completes, right-click the Model node and select Edit.
The Edit dialog box opens. Click Advanced.

- After the model build completes, right-click the Model node and select Advanced Settings.

4. The Advanced Settings has two grids:

- The Model Settings grid: The grid at the top lists the models built by the node.

- The lower part of the dialog box is a tabbed display of the following:

- Data Usage: The Data Usage tab displays information about which columns are selected for Model build, the mining type used for model building for each column, Data Type, Input, Auto Data Prep, and Rules. To view the details about the rules (Heuristics), click Show.

- Algorithm Settings

- Performance Settings

5. To view which columns are used as input for the Model build, select the model. In the Data Usage tab, the attributes used in model build rules indicate the heuristics applied to the attribute. For example, the mining type may be changed. For details, click Show.

6. You can change data usage information on a per-model basis, or you can change the data usage for several models at the same time.

To change data usage for several models, select the models by pressing the Ctrl key and clicking it simultaneously. Make the changes and click OK. The changes are made to the data usage for all selected models.

Note:

You can also turn Auto Data Prep off. This is not recommended. If you turn Auto Data Prep OFF, then you must ensure that the input is properly prepared for each algorithm.

Related Topics

- Advanced Settings Overview

Text

Text is available for any of the following data types: CHAR, VARCHAR2, BLOB, CLOB, NCHAR, or NVARCHAR2.

If you are connected to Oracle Database 12c Release 1 (12.1) and later, then you can specify text characteristics in the Text tab in the Edit Model Build dialog box.

If you specify text characteristics in the Text tab, then you are not required to use the Text nodes.

Note:

If you are connected to Oracle Database 11g Release 2 (11.2) or earlier, then use Text nodes. The Text tab is not available in Oracle Database 11g Release 2 and earlier.

To examine or specify text characteristics for data mining, either double-click the build node or right-click the node and select Edit from the context menu. Click the Text tab.

The Text tab enables you to modify the following:

- Categorical cutoff value: Enables you to control the cutoff used to determine whether a column should be considered as a Text or Categorical mining type. The cutoff value is an integer. It must be 10 or greater and less than or equal to 4000. The default value is 200.

- Default Transform Type: Specifies the default transformation type for column-level text settings. The values are:

- Token (Default): For Token as the transform type, the Default Settings are:

- Languages: Specifies the languages used in the documents. The default is English. To change this value, select an option from the drop-down list. You can select more than one language.

- Bigram: Select this option to mix the NORMAL token type with their bigram. For example, New York. The token type is BIGRAM.

- Stemming: By default, this option is not selected. Not all languages support stemming. If the language selected is English, Dutch, French, German, Italian, or Spanish, then stemming is automatically enabled. If Stemming is enabled, then stemmed words are returned for supported languages. Otherwise the original words are returned.

Note:

If both Bigram and Stemming are selected, then the token type is STEM_BIGRAM. If neither Bigram nor Stemming is selected, then token type is NORMAL.

- Stoplists: Specifies the stoplist to use. The default setting is to use the default stoplist. You can add stoplists or edit stoplists. If you select more than one language, and the selected stoplist is Default, then the default stop words for languages are added to the default stoplist from the repository. No duplicate stop words are added.

- Click to view the Stoplist Details. This opens the Stoplist Details dialog box.

- Click to add a new stoplist. This opens the New Stoplist Wizard.

- Tokens: Specify the following:

- Max number of tokens across all rows (document). The default is 3000.

- Min number of rows (document) required for a token

- Theme: If Theme is selected, then the Default Settings are as follows:

- Language: Specifies the languages used in the documents. The default is English. To change this value, select one from the drop-down list. You can select more than one language.

- Stoplists: Specifies the stoplist to use. The default setting is to use the default stoplist. You can add stoplists or edit stoplists. If you select more than one language and the selected stoplist is Default, then the default stop words for languages are added to the default stoplist (from the repository). No duplicate stop words are added.

- Themes: Specifies the maximum number of themes across all documents. The default is 3000.

- Synonym: The Synonym tab is enabled only if a thesaurus is loaded. By default, no thesaurus is loaded. You must manually load the default thesaurus provided by Oracle Text or upload your own thesaurus.

- Click Stoplists to open the Stoplist Editor. You can view, edit, and create stoplists.
You can use the same stoplist for all text columns.

Related Topics

- Oracle Text Concepts

- Text Nodes

- Stoplist Editor

Model Nodes Properties

The Properties of the Model node allows you to examine and change characteristics of the node.

You can view the properties of a Model Build node in any one of the following ways:

- Select the node and go to View and click Properties. Click the Properties tab if necessary.

- Right-click the node and select Go to Properties from the context menu.

In earlier releases, Properties was called Property Inspector. Properties of Model nodes have the following sections:

- Models
The Models section displays a list of the models defined in the node. By default, one model is built for each algorithm supported by the node.

- Build
The Build section displays information related to the model build. For models that have a target, such as Classification and Regression, the targets are listed. All models in a node have the same target.

- Test
The Test section is displayed for Classification and Regression models. They are the only models that can be tested.

- Details
The Details section displays the node name and comments about the node.

Models

The Models section displays a list of the models defined in the node. By default, one model is built for each algorithm supported by the node.

For each model, the name of the model, build information, the algorithm, and comments are listed in a grid. The Build column shows the time and date of the last successful build or if the model is not built or did not build successfully.

You can add, delete, or view models in the list. You can also indicate in which models are passed to subsequent nodes or not.

- To delete a model from the list, select it and click .

- To add a model, click . The Add Model dialog box opens.

- To view a model that was built successfully, select the model and click .

You can tune classification models from Properties pane.

- Output Column
The Output Column in the Model Settings grid controls passing of models to subsequent nodes.

- Add Model
In the Add Model dialog box, you can add a model to a node.

Related Topics

- Tuning Classification Models

Output Column

The Output Column in the Model Settings grid controls passing of models to subsequent nodes.

The default setting is to pass all models to subsequent nodes.

- To ignore a model, that is, to not pass it to subsequent nodes, click . The Output icon is replaced with the Ignore icon .

- To cancel the ignore, click the Ignore icon again. It changes to the output icon.

Add Model

In the Add Model dialog box, you can add a model to a node.

To add a model to a node:

1. In the Algorithm field, select an algorithm from the drop-down list. For example, if you add a model to a clustering node, then the available algorithm are k-Means and O-Cluster. A default model name is displayed. You can change the default model.

2. In the Comments field, add your comments, if any. This is an optional field.

3. Click OK.

Build

The Build section displays information related to the model build. For models that have a target, such as Classification and Regression, the targets are listed. All models in a node have the same target.

The Build section displays the following:

- Target: Displays the target. To change the target, select a new target from the drop-down list.

- Case ID: Displays the case ID of the model defined in this node. All the models in the node have the same case IDs. To edit the case IDs, select a different case ID from the drop-down list.

- Transaction ID: Displayed for Association models only. To change the transaction ID, click Edit.

- Item ID: Displayed for Association models only. To change the value, select an option from the drop-down list.

- Item Value: Displayed for Association models only. To change the value, select an option from the drop-down list.

Test

The Test section is displayed for Classification and Regression models. They are the only models that can be tested.

The Test section defines how tests are done. By default, all models are tested. All models in the node are tested in the same way.

Related Topics

- Classification Node Properties

- Regression Node Properties

Details

The Details section displays the node name and comments about the node.

You can change the name of the node and edit the comments in this section. The new node name and comments must meet the requirements.

Related Topics

- Node Name and Node Comments

Anomaly Detection Node

An Anomaly Detection node builds one or more models that detect rare occurrences, such as fraud, using the One-Class SVM algorithm.

By default, an Anomaly Detection node builds one model using the one-class SVM algorithm. All models in the node have the same case ID.

There are two ways to detect anomalies:

- Build and apply an Anomaly Detection model.

- Use an Anomaly Detection Query, one of the Predictive Query nodes.

An Anomaly Detection build can run in parallel. The following topics describe Anomaly Detection Nodes:

- Create Anomaly Detection Node
An Anomaly Detection node builds one or more models that detect rare occurrences, such as fraud, and other anomalies using the One-Class SVM algorithm.

- Edit Anomaly Detection Node
In the Edit Anomaly Detection Node dialog box, you can specify or change the characteristics of the models to build.

- Data for Model Build
Oracle Data Miner uses heuristic techniques on data for model build.

- Advanced Model Settings
The Advanced Settings dialog box lists all the models in the Model Settings section in the upper pane. You can add and delete models from the node.

- Anomaly Detection Node Properties
In the Properties pane, you can examine and change the characteristics or properties of a node.

- Anomaly Detection Node Context Menu
The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

Related Topics

- Anomaly Detection

- About Parallel Processing

- Anomaly Detection Query

Create Anomaly Detection Node

An Anomaly Detection node builds one or more models that detect rare occurrences, such as fraud, and other anomalies using the One-Class SVM algorithm.

The input for a Model node is any node that generates data as an output, including Transform nodes and Data nodes.

Note:

If the data includes text columns, then prepare the text columns using a Build Text node. If you are connected to Oracle Database 12c or later, then use Automatic Data Preparation.

To create an Anomaly Detection node:

First create a workflow and then identify or create a Data Source node.

1. In the Components pane, go to Workflow Editor. If the Components pane is not visible, then in the SQL Developer menu bar, go to View and click Components. Alternately, press Ctrl+Shift+P to dock the Components pane.

2. In the Workflow Editor, expand Models and click Anomaly Detection.

3. Drag and drop the node from the Components pane to the Workflow pane.
The node is added to the workflow. The GUI shows that the node has no data associated with it. Therefore, it cannot be run.

4. Move to the node that provides data for the build. Right-click and click Connect. Drag the line to the Anomaly Detection node and click again.

5. You can also specify a case ID, edit the data usage, and change the algorithm settings. To perform any of these tasks, right-click the node and select Edit.

6. The node is now ready to build. Right-click the node and click Run.

Related Topics

- Edit Anomaly Detection Node

- Build Text

Edit Anomaly Detection Node

In the Edit Anomaly Detection Node dialog box, you can specify or change the characteristics of the models to build.

To open the Edit Anomaly Detection Node dialog box, either double-click an Anomaly Detection node, or right-click an Anomaly Detection node and click Edit.

The Edit Anomaly Detection Node dialog box has the following tabs:

- Build (AD)
The Build tab for Anomaly Detection lists the models to be built and the Case ID.

- Partition
In the Partition tab, you can build partitioned models.

- Input
The Input tab specifies the input for model build.

- Sampling
The settings in the Sampling tab are applied to all models in the node. In the Sampling tab, you can specify the row size.

- Text
Text is available for any of the following data types: CHAR, VARCHAR2, BLOB, CLOB, NCHAR, or NVARCHAR2.

Related Topics

- Viewing and Changing Data Usage

Build (AD)

The Build tab for Anomaly Detection lists the models to be built and the Case ID.

Specify the following:

1. Select Case ID. Select an attribute from the Case ID list. This attribute must uniquely identify a case.

Note:

A case ID is not required. However, a case ID helps ensure build and test repeatability.

A case ID is required to generate GLM diagnostics.

If you specify a case ID, then all models in the node have the same case ID.

2. In the Models Settings list, specify the models you want to build. You can also perform the following tasks:

- To add a model, click . The Add Model dialog box opens.

- To edit a model, select the model and click . The Advanced Model Settings dialog box opens.

- To delete a model, select the model and click .

- To copy an existing model, select the model and click .

3. To complete the node definition, OK.

Related Topics

- Add Model (AD)

- Advanced Model Settings

Partition

In the Partition tab, you can build partitioned models.

- In the Maximum Number of Partitions field, set a value by clicking the arrows. This sets the partition cutoff value. The cutoff value must be greater than zero. If this option is not selected, then the native Oracle Data Miner cutoff value is used, which can be a very large value.

- Click Advanced Settings to set and select the type of partition build.

- To add columns for partitioning, click .

Note:

Only NUMBER and VARCHAR2 columns can be selected as partition columns. Case ID and Target columns cannot be selected as partition columns.

- To remove a partitioning column, select the columns and click .

- To move a column to the top, click .

- To move a column up, click

- To move a column down, click

- To move a column to the bottom, click

Input

The Input tab specifies the input for model build.

Determine inputs automatically (using heuristics) is selected by default for all models. Oracle Data Miner decides which attributes to use for input. For example, attributes that are almost constant may not be suitable for input. Oracle Data Miner also determines mining type and specifies that auto data preparation is performed for all attributes.

Note:
For R Build nodes, Auto Data Preparation is not performed.

After the node runs, rules are displayed explaining the heuristics. Click Show for detailed information.

You can change these selections. To do so, deselect Determine inputs automatically (using heuristics).

Related Topics

- Data Used for Model Building

Sampling

The settings in the Sampling tab are applied to all models in the node. In the Sampling tab, you can specify the row size.

By default, Sampling is set to OFF. To set it to ON:

1. Click ON, and then select:

- System Determined

- User Specified and specify the row size

2. Click OK.

Text

Text is available for any of the following data types: CHAR, VARCHAR2, BLOB, CLOB, NCHAR, or NVARCHAR2.

If you are connected to Oracle Database 12c Release 1 (12.1) and later, then you can specify text characteristics in the Text tab in the Edit Model Build dialog box.

If you specify text characteristics in the Text tab, then you are not required to use the Text nodes.

Note:

If you are connected to Oracle Database 11g Release 2 (11.2) or earlier, then use Text nodes. The Text tab is not available in Oracle Database 11g Release 2 and earlier.

To examine or specify text characteristics for data mining, either double-click the build node or right-click the node and select Edit from the context menu. Click the Text tab.

The Text tab enables you to modify the following:

- Categorical cutoff value: Enables you to control the cutoff used to determine whether a column should be considered as a Text or Categorical mining type. The cutoff value is an integer. It must be 10 or greater and less than or equal to 4000. The default value is 200.

- Default Transform Type: Specifies the default transformation type for column-level text settings. The values are:

- Token (Default): For Token as the transform type, the Default Settings are:

- Languages: Specifies the languages used in the documents. The default is English. To change this value, select an option from the drop-down list. You can select more than one language.

- Bigram: Select this option to mix the NORMAL token type with their bigram. For example, New York. The token type is BIGRAM.

- Stemming: By default, this option is not selected. Not all languages support stemming. If the language selected is English, Dutch, French, German, Italian, or Spanish, then stemming is automatically enabled. If Stemming is enabled, then stemmed words are returned for supported languages. Otherwise the original words are returned.

Note:

If both Bigram and Stemming are selected, then the token type is STEM_BIGRAM. If neither Bigram nor Stemming is selected, then token type is NORMAL.

- Stoplists: Specifies the stoplist to use. The default setting is to use the default stoplist. You can add stoplists or edit stoplists. If you select more than one language, and the selected stoplist is Default, then the default stop words for languages are added to the default stoplist from the repository. No duplicate stop words are added.

- Click to view the Stoplist Details. This opens the Stoplist Details dialog box.

- Click to add a new stoplist. This opens the New Stoplist Wizard.

- Tokens: Specify the following:

- Max number of tokens across all rows (document). The default is 3000.

- Min number of rows (document) required for a token

- Theme: If Theme is selected, then the Default Settings are as follows:

- Language: Specifies the languages used in the documents. The default is English. To change this value, select one from the drop-down list. You can select more than one language.

- Stoplists: Specifies the stoplist to use. The default setting is to use the default stoplist. You can add stoplists or edit stoplists. If you select more than one language and the selected stoplist is Default, then the default stop words for languages are added to the default stoplist (from the repository). No duplicate stop words are added.

- Themes: Specifies the maximum number of themes across all documents. The default is 3000.

- Synonym: The Synonym tab is enabled only if a thesaurus is loaded. By default, no thesaurus is loaded. You must manually load the default thesaurus provided by Oracle Text or upload your own thesaurus.

- Click Stoplists to open the Stoplist Editor. You can view, edit, and create stoplists.
You can use the same stoplist for all text columns.

Related Topics

- Oracle Text Concepts

- Text Nodes

- Stoplist Editor

Data for Model Build

Oracle Data Miner uses heuristic techniques on data for model build.

Oracle Data Miner uses heuristics to:

- Determine the attributes of the input data used for model build.

- Determine the mining type of each attribute.

Related Topics

- Data Used for Model Building

Advanced Model Settings

The Advanced Settings dialog box lists all the models in the Model Settings section in the upper pane. You can add and delete models from the node.

To change or view advanced settings, right-click the node and select Advanced Settings.

- To delete a model, select it and click .

- To add a model, click . The Add Model dialog box opens.

- To modify data usage of a model, select the model in the upper pane. Make the necessary modifications in the Data Usage tab.

- To modify the default algorithm, select the model in the upper pane. Make the necessary changes in the Algorithm Settings tab.

- Add Model (AD)
In the Add Model dialog box, you can add or change a model for the node.

- Data Usage
The Data Usage tab contains the data grid that lists all attributes in the data source.

Related Topics

- Advanced Settings Overview

- Algorithm Settings for AD

- Viewing and Changing Data Usage

- Algorithm Settings

Add Model (AD)

In the Add Model dialog box, you can add or change a model for the node.

The algorithm is already selected for you. To add a model:

1. In the Algorithm field, the selected algorithm is displayed. You can change this and select a different algorithm from the drop-down list.

2. In the Name field, enter a name for the model.

3. In the Comments field, add your comments, if any. This is an optional field.

4. Click OK.

Data Usage

The Data Usage tab contains the data grid that lists all attributes in the data source.

The Data Usage tab is not supported for the Association node. To modify any values, to see which attributes are not used as input, or to see mining types, select View in the lower pane.

You can change data usage information for several models at the same time. For each attribute, the grid lists displays the following:

- Attributes: This is the name of the attribute.

- Data Type: This is the Oracle Database data type of the attribute.

- Input: Indicates if the attribute is used to build the model. To change the input type, click Automatic. Then click the icon and select the new icon. For models that have a target, such as Classification and Regression models, the target is marked with a red target icon.

- The icon indicates that the attribute is used to build the model.

- The icon indicates that the attribute is ignored, that is, it is not used to build the model.

- Mining Type: This is the logical type of the attribute, either Numerical (numeric data), Categorical (character data), nested numerical, or nested categorical, text or custom text. If the attribute has a type that is not supported for mining, then the column is blank. Mining type is indicated by an icon. Move the cursor over the icon to see what the icon represents. To change the mining type, click Automatic and then click the type for the attribute. Select a new type from the list. You can change mining types as follows:

- Numerical can be changed to Categorical. Changing to Categorical casts the numerical value to string.

- Categorical.

- Nested Categorical and Nested Numerical cannot be changed.

- Auto Prep: If Auto Prep is selected, then automatic data preparation is performed on the attribute. If Auto Prep is not selected, then no automatic data preparation is performed for the attribute. In this case, you are required to perform any data preparation, such as normalization, that may be required by the algorithm used to build the model. No data preparation is done (or required) for target attributes. The default is to perform automatic data preparation.

- Rules: After a model runs, Rules describe the heuristics used. For details, click Show.

There are two types of reasons for not selecting an attribute as input:

- The attribute has a data type that is not supported by the algorithm used for model build.
For example, O-Cluster does not support nested data types such as DM_NESTED_NUMERICALS. If you use an attribute with type DM_NESTED_NUMERICALS to build a O-Cluster model, then the build fails.

- The attribute does not provide data useful for mining. For example, an attribute that has constant or nearly constant values.
If you include attributes of this kind, then the model has lower quality than if you exclude them.

Related Topics

- Viewing and Changing Data Usage

- Automatic Data Preparation (ADP)

Anomaly Detection Node Properties

In the Properties pane, you can examine and change the characteristics or properties of a node.

To view the properties of a node, click the node and click Properties. If the Properties pane is closed, then go to View and click Properties. Alternately, right-click the node and click Go to Properties.To view the properties of an Anomaly Detection node:

- Right-click the node and select Go to Properties from the context menu.

- If the Properties pane is closed, then go to View and click Properties.

Anomaly Detection Properties pane has the following sections:

- Models (AD)
The Models section displays a list of the models defined in the node. The default is to build one model.

- Build (AD)
The Build section displays the case ID for the models defined in this node.

- Partition
In the Partition tab, you can build partitioned models.

- Details
The Details section displays the node name and comments about the node.

Models (AD)

The Models section displays a list of the models defined in the node. The default is to build one model.

For each model, the name of the model, the build information, the algorithm, and comments are listed in a grid. The Build column shows the time and date of the last successful build or if the model is not built or did not build successfully.

You can add, delete, or view models in the list. You can also indicate in the which models are passed to subsequent nodes or not.

- To delete a model, select it and click .

- To add a model, click . The Add Model model dialog box opens.

- To view a model, click . The appropriate model viewer opens.

- To duplicate a model, select a model to duplicate and click .

- Output Column (AD)
The Output Column in the Model Settings grid controls passing of the models to subsequent nodes.

- Add Model (AD)
In the Add Model dialog box, you can add or change a model for the node.

Output Column (AD)

The Output Column in the Model Settings grid controls passing of the models to subsequent nodes.

The default is to pass all models to subsequent nodes.

- To ignore a model, click . The Output icon is replaced with the Ignore icon.

- To cancel the ignore, click the Ignore icon again. The icon changes to the Output icon.

Add Model (AD)

In the Add Model dialog box, you can add or change a model for the node.

The algorithm is already selected for you. To add a model:

1. In the Algorithm field, the selected algorithm is displayed. You can change this and select a different algorithm from the drop-down list.

2. In the Name field, enter a name for the model.

3. In the Comments field, add your comments, if any. This is an optional field.

4. Click OK.

Build (AD)

The Build section displays the case ID for the models defined in this node.

All the models in the node have the same case ID. To change the case ID, select a different attribute from the list.

Partition

In the Partition tab, you can build partitioned models.

- In the Maximum Number of Partitions field, set a value by clicking the arrows. This sets the partition cutoff value. The cutoff value must be greater than zero. If this option is not selected, then the native Oracle Data Miner cutoff value is used, which can be a very large value.

- Click Advanced Settings to set and select the type of partition build.

- To add columns for partitioning, click .

Note:

Only NUMBER and VARCHAR2 columns can be selected as partition columns. Case ID and Target columns cannot be selected as partition columns.

- To remove a partitioning column, select the columns and click .

- To move a column to the top, click .

- To move a column up, click

- To move a column down, click

- To move a column to the bottom, click

Details

The Details section displays the node name and comments about the node.

You can change the name of the node and edit the comments in this section. The new node name and comments must meet the requirements.

Related Topics

- Node Name and Node Comments

Anomaly Detection Node Context Menu

The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

To view the context menu options, right click the Anomaly Detection node. The following options are available in the context menu:

- Connect

- Edit. Opens the Edit Anomaly Detection Node dialog box.

- Advanced Settings. Opens the Advanced Model Settings.

- Validate Parents

- Run

- Force Run

- View Models. Opens the Anomaly Detection Model Viewer for the selected model.

- Generate Apply Chain

- Cut

- Copy

- Paste

- Extended Paste

- Select All

- Performance Settings. This opens the Edit Selected Node Settings dialog box, where you can set Parallel Settings and In-Memory settings for the node.

- Show Event Log

- Show Runtime Errors. Displayed only if there is an error.

- Show Validation Errors. Displayed only if there are validation errors.

- Navigate

Related Topics

- Edit Anomaly Detection Node

- Advanced Model Settings

Association Node

The Association node defines one or more Association models. To specify data for the build, connect a Data Source node to the Association node.

All models in an Association node have the same input data.

Note:

The data for an Association model must be in transactional format.

Association models could generate a very large number of rules with low confidence and support, or they could generate no rules at all.

An Association build can run in parallel.

This section contains the following topics:

- Behavior of the Association Node
By default, an Association node builds one model using the Apriori algorithm.

- Create Association Node
The data used to build an Association model must be in transactional format.

- Edit Association Build Node
The Association Build Node editor enables you to specify or change the characteristics of the models to build.

- Advanced Settings for Association Node
The Advanced Settings dialog box enables you to add or delete models, and modify the default algorithm settings for each model.

- Association Node Context Menu
The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

- Association Build Properties
In the Properties pane, you can examine and change the characteristics or properties of a node.

Related Topics

- About Parallel Processing

- About Oracle Database In-Memory

- Troubleshooting AR Models

Behavior of the Association Node

By default, an Association node builds one model using the Apriori algorithm.

The Apriori algorithm assumes the following:

- The data is transactional.

- The data has many missing values. The apriori algorithm interprets all missing values as sparse data, and it has its own mechanisms for handling sparse data.

All models in the node have the same case ID, item ID, and item value. The case ID can be two columns. For example, the data sources SH.SALES, CUST_ID and TIME_ID combined can be the case ID.

No automatic data preparation is done for an Association node. If you select a value for Item Value that is different from the default Existence, then you might have to prepare the data.

Related Topics

- Data for AR Models

Create Association Node

The data used to build an Association model must be in transactional format.

To create an Association node:

First, create a workflow and then identify or create a data source.

1. In the Components pane, go to Workflow Editor. If the Components pane is not visible, then in the SQL Developer menu bar, go to View and click Components. Alternately, press Ctrl+Shift+P to dock the Components pane.

2. In the Workflow Editor, expand Models, and click Association.

3. Drag and drop the node from the Components pane to the Workflow pane.
The node is added to the workflow. The GUI shows that the node has no data associated with it. Therefore, it cannot be run.

4. Move to the node that provides data for the build. Right-click the node and click Connect. Drag the line to the Association node and click again.

5. The Edit Association Build Node window opens.

6. For an Association node, specify the following:

- Transaction ID: Click to insert one or more Transaction IDs.

- Item ID: Select an option from the drop-down list.

- Value: Existence (default)

7. Click OK.

8. After you finish the node definition, the node is ready for build. Right-click the node and click Run.

Related Topics

- Edit Association Build Node

Edit Association Build Node

The Association Build Node editor enables you to specify or change the characteristics of the models to build.

To open the Edit Association Build Node dialog box, either double-click an Association node, or right-click an Association node and select Edit. The Edit Association Build Node dialog box comprises the following:

- Build
In the Build tab, you can provide the details required for a model build.

- Partition
In the Partition tab, you can build partitioned models.

- Filter
In the Filter tab, you can add items to filter. The items are sourced from the Data Source node, and not from the model.

- Aggregates
In the Aggregates dialog box, you can add items to be used for aggregation.

- Sampling
The settings in the Sampling tab are applied to all models in the node. In the Sampling tab, you can specify the row size.

Build

In the Build tab, you can provide the details required for a model build.

Specify these settings in the Build tab:

- Transaction IDs: These are a combination of attributes that uniquely identifies a transaction. To specify a transaction ID, click . The Select Columns dialog box opens. Move one or more attributes from the Available Attributes list to the Selected Attributes list. Click OK.

- Item ID: Identifies an item. Select an attribute from the list.

- Item Value: Existence (default). You can select an attribute from the drop-down list. This is an optional field.
The item value column may specify information such as the number of items (for example, three apples) or the type of the item (for example, Macintosh Apples).

If you select an attribute from the list, then the attribute must have less than 10 distinct values. The default value for the maximum distinct count is 10. You can change the value in Model Build Preferences for Association.

Note:

If you specify an attribute for Item Value, then you might have to prepare the data.

You can perform the following tasks:

- Add a model: Click . The Add Model dialog box opens.

- Delete a model: Select the model and click .

- Edit a model: Select the model and click . The Advanced Settings for Association Node dialog box opens. Here, you can specify Model settings or Algorithm settings.

- Copy an existing model: Select the model and click .

At this point, you can click OK to finish the model definition.

- Select Columns (AR)
In the Select Column dialog box, you can add or remove attributes to be included in or excluded from model build.

Related Topics

- Add Model (AR)

- Advanced Settings for Association Node

- Model Build

Select Columns (AR)

In the Select Column dialog box, you can add or remove attributes to be included in or excluded from model build.

To select attributes:

1. Select one or more attributes in the Available Attributes list.

2. Use the arrows between the lists to move the selections to the Selected Attributes list.

3. Click OK.

Partition

In the Partition tab, you can build partitioned models.

- In the Maximum Number of Partitions field, set a value by clicking the arrows. This sets the partition cutoff value. The cutoff value must be greater than zero. If this option is not selected, then the native Oracle Data Miner cutoff value is used, which can be a very large value.

- Click Advanced Settings to set and select the type of partition build.

- To add columns for partitioning, click .

Note:

Only NUMBER and VARCHAR2 columns can be selected as partition columns. Case ID and Target columns cannot be selected as partition columns.

- To remove a partitioning column, select the columns and click .

- To move a column to the top, click .

- To move a column up, click

- To move a column down, click

- To move a column to the bottom, click

- Advanced Settings
In the Advanced Settings dialog box, you can select and set the type of partition build.

Advanced Settings

In the Advanced Settings dialog box, you can select and set the type of partition build.

To set the type of partition build:

1. In the Partition Build Type field, select any one of the following options:

- Combination of the two

- Partition is processed at a single slave

- Partition is processes across slaves

2. Click OK.

Filter

In the Filter tab, you can add items to filter. The items are sourced from the Data Source node, and not from the model.

1. Click Enable Filters.
In the Include section, you can include items. In the Exclude section you can exclude items from the filter.

2. Expand Include to filter and include items in the filter rule. You can add and delete items, both in the Antecedent and Consequent section of the rule.

- Click to add items to the inclusion rule. The Find Items dialog box opens.

- Click to remove items from the rule.

3. Expand Exclude to filter and exclude items from the filter rule.

- Click to add items to the exclusion rule. The Find Items dialog box opens.

- Click to remove items from the rule.

4. Click Advanced Settings. In the Preprocess Input Data dialog box, you may select the option Preprocess Input Data to Extract Items. If you select this option and run the node, then an internal table is generated that contains all distinct item values along with their respective total count and support. This table is used in place of querying the underlying data, thereby significantly improving the UI interaction.

5. Click OK.

- Find Items
In the Find Items dialog box, you can search and add items to be included in the filter rule or excluded from the filter rule.

Find Items

In the Find Items dialog box, you can search and add items to be included in the filter rule or excluded from the filter rule.

1. In the Search For field, enter the name of the item to search.

2. In the Settings section, provide additional information about the item in the following fields:

- Sort By

- Fetch Size

- Sample Size

- Use All Data

3. Click Find.

4. In the Items Found section, select the items that you want to add to the filter rules, and click Add. The items are now displayed in the Selected Items section.

5. Click OK.

Aggregates

In the Aggregates dialog box, you can add items to be used for aggregation.

To include items for aggregation or exclude from aggregation:

1. Select the items that you want to add in the Available section.

2. Click the arrows as applicable to move the items to the Selected section.

3. Click OK.
This adds or removes the items to be used in the association rules.

Sampling

The settings in the Sampling tab are applied to all models in the node. In the Sampling tab, you can specify the row size.

By default, Sampling is set to OFF. To set it to ON:

1. Click ON, and then select:

- System Determined

- User Specified and specify the row size

2. Click OK.

Advanced Settings for Association Node

The Advanced Settings dialog box enables you to add or delete models, and modify the default algorithm settings for each model.

The upper pane of the dialog box lists all the models in the node. You can add and delete models.

1. To open the Advanced Settings dialog box:

- Click in the Edit Association Build Node dialog box.

- Right-click the node and click Advanced Settings.

The Advanced Settings dialog box opens.

2. You can perform the following tasks:

a. Delete a model.

b. Add a model.

c. Change algorithm settings. To change algorithm settings, select a model in the upper pane. In the Algorithm Settings tab, you can change maximum rule length, minimum confidence, and minimum support.

3. Click OK.

Note:
It is possible for an Association model to generate a very large number of rules or no rules at all.

Related Topics

- Advanced Settings Overview

- Algorithm Settings

- Add Model

- Edit Association Build Node

Association Node Context Menu

The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

To view the context menu options, right click the node. The following options are available in the Association node context menu:

- Connect

- Edit. Opens the Edit Association Build Node dialog box.

- Advanced Settings. Opens the Algorithm Settings dialog box.

- Run

- Force Run

- View Models. Opens the AR Model Viewer for the selected model.

- Generate Apply Chain

- Cut

- Copy

- Paste

- Extended Paste

- Select All

- Performance Settings. This opens the Edit Selected Node Settings dialog box, where you can set Parallel Settings and In-Memory settings for the node.

- Show Event Log

- Show Runtime Errors. Displayed only if there is an error.

- Show Validation Errors. Displayed only if there are validation errors.

- Navigate

Related Topics

- AR Model Viewer

- Algorithm Settings for Association Rules

Association Build Properties

In the Properties pane, you can examine and change the characteristics or properties of a node.

To view the properties of a node, click the node and click Properties. If the Properties pane is closed, then go to View and click Properties. Alternately, right-click the node and click Go to Properties.

The Association Build node Properties pane has the following sections:

- Models (AR)
The Models section displays a list of the models defined in the node. The default is to build one model.

- Build (AR)
The Build section displays the transaction ID, item ID and item value of the models defined in the node.

- Partition
In the Partition tab, you can build partitioned models.

- Filter
In the Filter tab, you can add items to filter. The items are sourced from the Data Source node, and not from the model.

- Aggregates
In the Aggregates dialog box, you can add items to be used for aggregation.

- Sampling
The settings in the Sampling tab are applied to all models in the node. In the Sampling tab, you can specify the row size.

- Details
The Details section displays the node name and comments about the node.

Models (AR)

The Models section displays a list of the models defined in the node. The default is to build one model.

For each model, the name of the model, build information, the algorithm, and comments are listed in a grid. The Build column shows the time and date of the last successful build or if the model is not built or did not build successfully.

You can add, delete, or view models in the list. You can also indicate which models are passed to subsequent nodes or not.

- To delete a model from the list, select it and click .

- To add a model, click . The Add Model dialog box opens.

- To view a model that is built successfully, click . The appropriate model view opens.

- To make a copy of a model, select the model and click .

- Add Model (AR)

- Output Column (AR)

Add Model (AR)

The algorithm is already selected for you. To add a model to the list:

1. Accept or change the model name.

2. In the Comments field, add comments, if any. This is optional.

3. Click OK. This adds the new model to the list. The new model has the same build characteristics as existing models. It also has the default values for advanced settings.

Output Column (AR)

The Output column in the Model Settings grid controls the passing of models to subsequent nodes. By default, all models are passed to subsequent nodes. You can perform the following tasks:

- To ignore a model, click . The icon changes to .

- To cancel an ignored model, click the ignore icon again. The icon changes to the Output icon.

Build (AR)

The Build section displays the transaction ID, item ID and item value of the models defined in the node.

All models in the node have the same transaction ID, item ID and item value. The information that is displayed are:

- Transaction IDs: Click Edit to change the transaction ID.

- Item ID: You can select a different item ID from the drop-down list.

- Item Value: You can select a different item value from the drop-down list.

Partition

In the Partition tab, you can build partitioned models.

- In the Maximum Number of Partitions field, set a value by clicking the arrows. This sets the partition cutoff value. The cutoff value must be greater than zero. If this option is not selected, then the native Oracle Data Miner cutoff value is used, which can be a very large value.

- Click Advanced Settings to set and select the type of partition build.

- To add columns for partitioning, click .

Note:

Only NUMBER and VARCHAR2 columns can be selected as partition columns. Case ID and Target columns cannot be selected as partition columns.

- To remove a partitioning column, select the columns and click .

- To move a column to the top, click .

- To move a column up, click

- To move a column down, click

- To move a column to the bottom, click

Filter

In the Filter tab, you can add items to filter. The items are sourced from the Data Source node, and not from the model.

1. Click Enable Filters.
In the Include section, you can include items. In the Exclude section you can exclude items from the filter.

2. Expand Include to filter and include items in the filter rule. You can add and delete items, both in the Antecedent and Consequent section of the rule.

- Click to add items to the inclusion rule. The Find Items dialog box opens.

- Click to remove items from the rule.

3. Expand Exclude to filter and exclude items from the filter rule.

- Click to add items to the exclusion rule. The Find Items dialog box opens.

- Click to remove items from the rule.

4. Click Advanced Settings. In the Preprocess Input Data dialog box, you may select the option Preprocess Input Data to Extract Items. If you select this option and run the node, then an internal table is generated that contains all distinct item values along with their respective total count and support. This table is used in place of querying the underlying data, thereby significantly improving the UI interaction.

5. Click OK.

Aggregates

In the Aggregates dialog box, you can add items to be used for aggregation.

To include items for aggregation or exclude from aggregation:

1. Select the items that you want to add in the Available section.

2. Click the arrows as applicable to move the items to the Selected section.

3. Click OK.
This adds or removes the items to be used in the association rules.

Sampling

The settings in the Sampling tab are applied to all models in the node. In the Sampling tab, you can specify the row size.

By default, Sampling is set to OFF. To set it to ON:

1. Click ON, and then select:

- System Determined

- User Specified and specify the row size

2. Click OK.

Details

The Details section displays the node name and comments about the node.

You can change the name of the node and edit the comments in this section. The new node name and comments must meet the requirements.

Related Topics

- Node Name and Node Comments

Classification Node

The Classification node defines one or more classification models to build and to test.

To specify data for the build, connect a Data Source node to the Classification node. The models in a Classification node all have the same target and case ID. You can only specify one target. A Classification build can run in parallel.

There are two ways to make classification predictions:

- By building and testing a classification model. This can be done by using a classification node, and then applying the model to the new data to make classifications.

- By using a prediction query, which is one of the predictive queries.

The section contains the following topics:

- Default Behavior for Classification Node
The default behavior of Classification node is based on certain algorithms, testing and tuning of models, Case ID and so on.

- Create a Classification Node
The Classification node defines one or more classification models to build and to test.

- Data for Model Build
Oracle Data Miner uses heuristic techniques on data for model build.

- Edit Classification Build Node
In the Edit Classification Build Node dialog box, you can specify or change the characteristics of the models to build.

- Advanced Settings for Classification Models
The Advanced Settings dialog box enables you to edit data usage and other model specifications, add and remove models from the node.

- Classification Node Properties
The Classification node properties enables you to view and change information about model build and test.

- Classification Build Node Context Menu
The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

Related Topics

- About Parallel Processing

- About Oracle Database In-Memory

- Prediction Query

Default Behavior for Classification Node

The default behavior of Classification node is based on certain algorithms, testing and tuning of models, Case ID and so on.

- Algorithms used: For a binary target, the Classification node builds models using the following four algorithms:

- GLM Classification Models

- SVM Classification Models

- Decision Tree Algorithm

- Naive Bayes

If the target is not binary, then GLM is not built by default. You can explicitly add a GLM model to the node. The models must have the same build data and same target.

Note:

If do not want to create a particular model, then delete the model from the list of models. The blue check mark to the left side of the model name selects models to be used in subsequent nodes. It does not select models to build.

- Testing of models: By default, the models are all tested. The test data is created by randomly splitting the build data into a build data set and a test data set. The default ratio for the split is 60:40. That is, 60 percent build and 40 percent test. Oracle Data Miner uses compression when it creates the build and test tables when appropriate.

- Connecting nodes: You can connect both a build Data Source node and a test Data Source node to the Build node.

- Testing models: You can test Classification models using a Test node along with separate test data.

- Interpreting test results

- Tuning models: After testing a classification, you can tune each model.

- Case ID: The case ID is optional. However, if you do not specify a case ID, then the processing will be slower.

Related Topics

- Create Table Node and Compression

- Classification Model Test Viewer

- Test Node

- Tuning Classification Models

Create a Classification Node

The Classification node defines one or more classification models to build and to test.

First, create a workflow. Then, identify or create a Data Source node for the Classification node.

To create a Classification node:

1. In the Components pane, go to Workflow Editor. If the Components pane is not visible, then in the SQL Developer menu bar, go to View and click Components. Alternately, press Ctrl+Shift+P to dock the Components pane.

2. In the Workflow Editor expand Models, and click Classification.

3. Drag and drop the node from the Components pane to the Workflow pane.
The node is added to the workflow. The GUI shows that the node has no data associated with it. Therefore, it cannot be run.

4. Move to the node that provides data for the build. Right-click and click Connect. Drag the line to the Classification node and click again.

5. The Edit Classification Build Node dialog box opens. You must specify a target. All models in the node have the same target. The target cannot be text.

6. To specify a separate Data Source node for test, connect a second Data Source node to the build node. This is optional.

7. After you finish the edit operation and connect the optional test data source, the node should be ready to build. Right-click the node and select Run from the menu.

If you specified a test data source, when the node runs, then the connection from the build data source is labeled Build and the connection from the test data source is labeled Test.

Related Topics

- Edit Classification Build Node

Data for Model Build

Oracle Data Miner uses heuristic techniques on data for model build.

Oracle Data Miner uses heuristics to:

- Determine the attributes of the input data used for model build.

- Determine the mining type of each attribute.

Related Topics

- Data Used for Model Building

Edit Classification Build Node

In the Edit Classification Build Node dialog box, you can specify or change the characteristics of the models to build.

To open the Edit Classification Build Node dialog box, either double-click a Classification Node, or right-click a Classification node and select Edit.

The Edit Classification Build Node dialog box has the following tabs:

- Build (Classification)
The Build node enables you to specify or change the characteristics of the models to build.

- Partition
In the Partition tab, you can build partitioned models.

- Sampling
The settings in the Sampling tab are applied to all models in the node. In the Sampling tab, you can specify the row size.

- Input
The Input tab specifies the input for model build.

- Text
Text is available for any of the following data types: CHAR, VARCHAR2, BLOB, CLOB, NCHAR, or NVARCHAR2.

Related Topics

- Viewing and Changing Data Usage

Build (Classification)

The Build node enables you to specify or change the characteristics of the models to build.

To edit the characteristics of the models to build, follow these steps:

1. In the Target field, select the target from the drop-down list. The list consist of attributes from the table or view specified in the Data Source node that is connected to the build node.

You must specify a target. All models in the node have the same target.

2. In the Case ID field, select one attribute from the drop-down list. This attribute must uniquely identify a case. If you specify a case ID, then all models in the node will have the same case ID.

Note:

If you do not specify a case ID, then the processing will be slower because a table must be generated.

The case ID is required to generate GLM diagnostics.

A case ID is required if a column in the input data is a nested column. That is, very dense and deep (lots of name-value pairs). If there is no case ID, then the sorting operations may fail.

3. In the Model Settings section, select which models you want to build. For a Classification node with a binary target, Naive Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), and Generalized Linear Models (GLM) models are specified by default.

- To delete a model, select the model and click .

- To edit a model, select the model and click .

- To add models, click .

- To copy an existing model, select the model to be copied and click .

By default, the model is tested using a test data set created by splitting the build data set. If you do not want to test the model in this way, then go to the Classification Test Node section in Classification node Properties pane. You can instead use a Test Node, and a test data source node to test the model.

- No Case ID
If a case ID is not supplied, then Oracle Data Miner creates a table for the all the input data that contains a generated case ID using the row number.

Related Topics

- Advanced Settings for Classification Models

- Classification Node Test

No Case ID

If a case ID is not supplied, then Oracle Data Miner creates a table for the all the input data that contains a generated case ID using the row number.

This table is used as the source to create the build and test random sample views. The generated case ID is constant for all queries. This ensures that consistent test results are generated.

Partition

In the Partition tab, you can build partitioned models.

- In the Maximum Number of Partitions field, set a value by clicking the arrows. This sets the partition cutoff value. The cutoff value must be greater than zero. If this option is not selected, then the native Oracle Data Miner cutoff value is used, which can be a very large value.

- Click Advanced Settings to set and select the type of partition build.

- To add columns for partitioning, click .

Note:

Only NUMBER and VARCHAR2 columns can be selected as partition columns. Case ID and Target columns cannot be selected as partition columns.

- To remove a partitioning column, select the columns and click .

- To move a column to the top, click .

- To move a column up, click

- To move a column down, click

- To move a column to the bottom, click

- Add Partition Column
In the Add Partition Column dialog box, you can add columns for partitioning. Partition columns are used to partition build models.

Add Partition Column

In the Add Partition Column dialog box, you can add columns for partitioning. Partition columns are used to partition build models.

Select the columns that you want to partition in the Available Attributes list, and click the arrows to move them the Selected Attributes list. In the Available Attributes list, only the columns with the supported data types are displayed.

Sampling

The settings in the Sampling tab are applied to all models in the node. In the Sampling tab, you can specify the row size.

By default, Sampling is set to OFF. To set it to ON:

1. Click ON, and then select:

- System Determined

- User Specified and specify the row size

2. Click OK.

Input

The Input tab specifies the input for model build.

Determine inputs automatically (using heuristics) is selected by default for all models. Oracle Data Miner decides which attributes to use for input. For example, attributes that are almost constant may not be suitable for input. Oracle Data Miner also determines mining type and specifies that auto data preparation is performed for all attributes.

Note:
For R Build nodes, Auto Data Preparation is not performed.

After the node runs, rules are displayed explaining the heuristics. Click Show for detailed information.

You can change these selections. To do so, deselect Determine inputs automatically (using heuristics).

Related Topics

- Data Used for Model Building

Text

Text is available for any of the following data types: CHAR, VARCHAR2, BLOB, CLOB, NCHAR, or NVARCHAR2.

If you are connected to Oracle Database 12c Release 1 (12.1) and later, then you can specify text characteristics in the Text tab in the Edit Model Build dialog box.

If you specify text characteristics in the Text tab, then you are not required to use the Text nodes.

Note:

If you are connected to Oracle Database 11g Release 2 (11.2) or earlier, then use Text nodes. The Text tab is not available in Oracle Database 11g Release 2 and earlier.

To examine or specify text characteristics for data mining, either double-click the build node or right-click the node and select Edit from the context menu. Click the Text tab.

The Text tab enables you to modify the following:

- Categorical cutoff value: Enables you to control the cutoff used to determine whether a column should be considered as a Text or Categorical mining type. The cutoff value is an integer. It must be 10 or greater and less than or equal to 4000. The default value is 200.

- Default Transform Type: Specifies the default transformation type for column-level text settings. The values are:

- Token (Default): For Token as the transform type, the Default Settings are:

- Languages: Specifies the languages used in the documents. The default is English. To change this value, select an option from the drop-down list. You can select more than one language.

- Bigram: Select this option to mix the NORMAL token type with their bigram. For example, New York. The token type is BIGRAM.

- Stemming: By default, this option is not selected. Not all languages support stemming. If the language selected is English, Dutch, French, German, Italian, or Spanish, then stemming is automatically enabled. If Stemming is enabled, then stemmed words are returned for supported languages. Otherwise the original words are returned.

Note:

If both Bigram and Stemming are selected, then the token type is STEM_BIGRAM. If neither Bigram nor Stemming is selected, then token type is NORMAL.

- Stoplists: Specifies the stoplist to use. The default setting is to use the default stoplist. You can add stoplists or edit stoplists. If you select more than one language, and the selected stoplist is Default, then the default stop words for languages are added to the default stoplist from the repository. No duplicate stop words are added.

- Click to view the Stoplist Details. This opens the Stoplist Details dialog box.

- Click to add a new stoplist. This opens the New Stoplist Wizard.

- Tokens: Specify the following:

- Max number of tokens across all rows (document). The default is 3000.

- Min number of rows (document) required for a token

- Theme: If Theme is selected, then the Default Settings are as follows:

- Language: Specifies the languages used in the documents. The default is English. To change this value, select one from the drop-down list. You can select more than one language.

- Stoplists: Specifies the stoplist to use. The default setting is to use the default stoplist. You can add stoplists or edit stoplists. If you select more than one language and the selected stoplist is Default, then the default stop words for languages are added to the default stoplist (from the repository). No duplicate stop words are added.

- Themes: Specifies the maximum number of themes across all documents. The default is 3000.

- Synonym: The Synonym tab is enabled only if a thesaurus is loaded. By default, no thesaurus is loaded. You must manually load the default thesaurus provided by Oracle Text or upload your own thesaurus.

- Click Stoplists to open the Stoplist Editor. You can view, edit, and create stoplists.
You can use the same stoplist for all text columns.

Related Topics

- Oracle Text Concepts

- Text Nodes

- Stoplist Editor

Advanced Settings for Classification Models

The Advanced Settings dialog box enables you to edit data usage and other model specifications, add and remove models from the node.

The Advanced Settings dialog box comprises the following settings:

- Data usage

- Algorithm settings

- Performance settings

To change or view advanced settings, click in the Edit Classification Build Node dialog box dialog box. Alternately, right-click the Classification Build node and click Advanced Settings.

The Advanced Settings dialog box lists all of the models in the node in the upper pane. You can add models and delete models in the upper pane of the dialog box.

In the lower pane, you can view or edit the following for the model selected in the upper pane:

- Data Usage

- Algorithm Settings
The settings that can be changed depend on the algorithm:

- Decision Tree Algorithm Settings

- GLM Classification Algorithm Settings

- Naive Bayes Algorithm Settings

- SVM Classification Algorithm Settings

- Performance Settings

- Add Models

Add Models

To add a model to the list, click . The Add Model dialog box opens.

- Add Model (Classification)
In the Add Model dialog box, you can add additional models.

Add Model (Classification)

In the Add Model dialog box, you can add additional models.

To add a model:

1. In the Algorithm field, select an algorithm.

2. In the Name field, a default name is displayed. You can use the default or rename the model.

3. In the Comments field, you can enter comments, if any. This is an optional field.

4. Click OK to add the model to the node.

Classification Node Properties

The Classification node properties enables you to view and change information about model build and test.

Specify a target before building Classification models. You can specify a case ID. If you do not specify a case ID, then the processing will be slower.

If you are unable to view Properties, then go to View and click Properties. Alternately, right-click the node and click Go to Properties.

Classification node Properties pane has these sections:

- Classification Node Models
The Classification node lists the models that are built when the node runs. By default, the Classification Build node creates three classification models

- Classification Node Build
The Build section displays the target and the case ID. The Build node must be connected to a Data Source node.

- Classification Node Test
The Test section specifies the data used for test and which tests to perform.

- Partition
In the Partition tab, you can build partitioned models.

- Details
The Details section displays the node name and comments about the node.

Classification Node Models

The Classification node lists the models that are built when the node runs. By default, the Classification Build node creates three classification models

Each Classification Model uses a different classification algorithm:

- Support Vector Machine (SVM)

- Naive Bayes (NB)

- Decision Tree (DT)

- Generalized Linear Models (GLM). This algorithm is used as default, only if the target is binary. For multi-class targets, you can also specify the GLM algorithm if you add a model.

Model Setting lists the models that are built.

You can perform the following tasks:

- Add: To add a model, click . The Add Model dialog box opens.

- Delete: To delete the models, select it and click .

- Compare Test Results: If models were tested, then you can compare test results by selecting two or more models and clicking .

- View: If a model built successfully, then you can view the model by selecting the model and clicking . The Model viewer depends on the algorithm used to create the model.

- GLM Classification Model Viewer

- Decision Tree Model Viewer

- Naive Bayes Model Viewer

- SVM Classification Model Viewer

- Duplicate: To copy a model, select the model and click .

- Tune Models: To tune models, select the model and click . This option is not available for partitioned models.

You can also indicate which models are passed to subsequent nodes or not.

- Classification Node Output Column

Related Topics

- Add Model (Classification)

- Tuning Classification Models

Classification Node Output Column

The Output column in the Model Settings grid controls the passing of models to subsequent nodes. By default, all models are passed to subsequent nodes.

- To ignore a model, that is, not to pass it to subsequent nodes, click . The icon changes to the Ignore icon

- To cancel the ignore, click the Ignore icon again. It changes to the output icon.

Classification Node Build

The Build section displays the target and the case ID. The Build node must be connected to a Data Source node.

You can perform the following tasks:

- Target: You can select a target from the Target drop-down list.

- Case ID: To change or select a case ID, select one attribute from the case ID drop-down list. This attribute uniquely identifies a case. case ID is an optional field. If you do not select a case ID, then the processing will be slower.

Classification Node Test

The Test section specifies the data used for test and which tests to perform.

You can set the following settings:

- Perform Test: Select this option to test the Classification Node. The default setting is to test all models built using the test data that is created by randomly splitting the build data into two subsets. By default, the following tests are performed:

- Performance Metrics

- Performance Matrix

- ROC Curve (Binary Class only)

- Lift and Profit: Lift and profit for the top 5 target classes by frequency. Click Edit. The Target Values Selection dialog box opens.

- Generate Selected Test Results for Tuning: If you plan to tune the models, then you must test the models in the Build node, not in a Test node.

Note:

This option is not available for partitioned models.

- Test Data: Select any one of the following options, by which Test Data is created:

- Use all Mining Build Data for Testing

- Use Split Build Data for Testing

- Split for Test (%)

- Create Split as: Table (default)

- Use a Test Data Source for Testing: Select this option to connect the Test Data Source to the Build node, after you connect the Build data.

Note:

Another way to test a model is to use a Test node.

- Target Values Selection
In the Target Values Selection dialog box, you can change the number of target values by changing the frequency count.

Related Topics

- Test Node

- Testing Classification Models

Target Values Selection

In the Target Values Selection dialog box, you can change the number of target values by changing the frequency count.

The Target Values Selection dialog box displays the number of target values selected. The default option Automatic is to use the top five target class values by frequency. You can change the number of target values by changing the frequency count. You can also select the option Use Lowest Occurring.

- Automatic: By default, use the top five target class values by frequency.

- Frequency Count: You can change the number of target values by changing the values in this value.

- Use Lowest Occurring

- Use Highest Occurring

- Custom: Select this option to specify specific target values. Then, move the values from Available Values to Selected Values.

Partition

In the Partition tab, you can build partitioned models.

- In the Maximum Number of Partitions field, set a value by clicking the arrows. This sets the partition cutoff value. The cutoff value must be greater than zero. If this option is not selected, then the native Oracle Data Miner cutoff value is used, which can be a very large value.

- Click Advanced Settings to set and select the type of partition build.

- To add columns for partitioning, click .

Note:

Only NUMBER and VARCHAR2 columns can be selected as partition columns. Case ID and Target columns cannot be selected as partition columns.

- To remove a partitioning column, select the columns and click .

- To move a column to the top, click .

- To move a column up, click

- To move a column down, click

- To move a column to the bottom, click

Details

The Details section displays the node name and comments about the node.

You can change the name of the node and edit the comments in this section. The new node name and comments must meet the requirements.

Related Topics

- Node Name and Node Comments

Classification Build Node Context Menu

The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

To view the context menu options, right click the node. The following options are available in the context menu:

- Connect

- Edit. Opens the Edit Classification Node dialog box.

- Advanced Settings. Opens the Advanced Settings for Classification Models dialog box.

- Validate Parents

- Run

- Force Run

- View Models

- View Test Results

- Compare Test Results

- Generate Apply Chain

- Cut

- Copy

- Paste

- Extended Paste

- Select All

- Performance Settings. This opens the Edit Selected Node Settings dialog box, where you can set Parallel Settings and In-Memory settings for the node.

- Show Event Log

- Show Runtime Errors. Displayed only if there is an error.

- Show Validation Errors. Displayed only if there are validation errors.

- Navigate

- View Test Results

- Compare Test Results

Related Topics

- Edit Classification Build Node

- Advanced Settings for Classification Models

View Test Results

Select a model and then view the test results for the model.

Related Topics

- Compare Classification Test Results

Compare Test Results

You can compare all successfully built models in the node by comparing the text results.

Related Topics

- Compare Test Results Viewer

Clustering Node

A Clustering node builds clustering models using the k-Means, O-Cluster, and Expectation Maximization algorithms.

There are two ways to cluster data:

- By building a Clustering model: Use a Classification node. Then apply the model to new data to create clusters.

- By using a Clustering query, which is one of the predictive queries.

A Clustering build can run in parallel.

Note:

Expectation Maximization models require Oracle Database 12c Release 1 (12.1) or later.

This section contains the following topics:

- Default Behavior for Clustering Node
A Clustering node builds three models using three different algorithms.

- Create Clustering Build Node
You create a Clustering node to build clustering models using the k-Means, O-CLuster, and Expectation Maximization algorithms.

- Data for Model Build
Oracle Data Miner uses heuristic techniques on data for model build.

- Edit Clustering Build Node
The Edit Clustering Build Node dialog box you can specify or change the characteristics of the models to build.

- Advanced Settings for Clustering Models
In the Advanced Settings dialog box, you can review and change settings related to data usage and algorithms used in the model.

- Clustering Build Node Properties
In the Properties pane, you can examine and change the characteristics or properties of a node.

- Clustering Build Node Context Menu
The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

Related Topics

- About Parallel Processing

- About Oracle Database In-Memory

- Clustering Query

Default Behavior for Clustering Node

A Clustering node builds three models using three different algorithms.

The algorithms used by the Clustering node are:

- k-Means algorithm (KM)

- Orthogonal Partitioning Clustering (OC)

- Expectation Maximization (EM). For EM, Oracle Database 12c Release 1 (12.1) or later is required.

A case ID is optional.

The models all have the same build data.

Note:

If do not want to create a model, then delete the model from the list of models. The blue check mark to the left of the model name selects models to be used in subsequent nodes, such as Apply. It does not select models to build.

Related Topics

- k-Means Algorithm

- Orthogonal Partitioning Clustering

- Expectation Maximization

Create Clustering Build Node

You create a Clustering node to build clustering models using the k-Means, O-CLuster, and Expectation Maximization algorithms.

First create a workflow. Then, identify or create a Data Source node.

To create a Clustering node and attach data to it:

1. In the Components pane, go to Workflow Editor. If the Components pane is not visible, then in the SQL Developer menu bar, go to View and click Components. Alternately, press Ctrl+Shift+P to dock the Components pane.

2. In the Workflow Editor, expand Models and click Clustering.

3. Drag and drop the node from the Components pane to the Workflow pane.
The node is added to the workflow. The GUI shows that the node has no data associated with it. Therefore, it cannot be run.

4. Move to the node that provides data for the build. Right-click the node and click Connect.Drag the line to the Classification node and click again.

5. Right-click the Clustering node and click Run. The node runs and builds the models.

Data for Model Build

Oracle Data Miner uses heuristic techniques on data for model build.

Oracle Data Miner uses heuristics to:

- Determine the attributes of the input data used for model build.

- Determine the mining type of each attribute.

Related Topics

- Data Used for Model Building

Edit Clustering Build Node

The Edit Clustering Build Node dialog box you can specify or change the characteristics of the models to build.

To open the Edit Clustering Build Node dialog box, double-click a Clustering node. Alternately, you can right-click a Clustering node and select Edit.

The Edit Clustering Build Node dialog box has three tabs:

- Build (Clustering)
The Build tab enables you to specify or change the characteristics of the models to build.

- Partition
In the Partition tab, you can build partitioned models.

- Sampling
The settings in the Sampling tab are applied to all models in the node. In the Sampling tab, you can specify the row size.

- Input
The Input tab specifies the input for model build.

- Text
Text is available for any of the following data types: CHAR, VARCHAR2, BLOB, CLOB, NCHAR, or NVARCHAR2.

Related Topics

- Viewing and Changing Data Usage

Build (Clustering)

The Build tab enables you to specify or change the characteristics of the models to build.

To edit the characteristics of the models to build:

1. In the Case ID field, select an attribute from the drop-down list. This attribute must uniquely identify a case.

Note:

A case ID is not required. However, a case ID helps ensure build and test repeatability.

If you specify a case ID, then all models in the node have the same case ID.

2. In the Model Settings list, select the models you want to build. For a Clustering node, you can build models using the following algorithms:

- k- Means (KM)

- Orthogonal Partitioning Clustering (OC)

- Expectation Maximization (EM). For this algorithm, Oracle Database 12c release 1 (12.1) or later is required.

You can perform the following tasks:

- Delete: To delete any models, select the models and click .

- Add: To add a model, click .

- Copy: To copy a model, select the model and click .

3. Click OK.

- Add Model (Clustering)
In the Add Model dialog box, you can add models to the Clustering node.

Related Topics

- k-Means

- Orthogonal Partitioning Clustering

- Expectation Maximization

Add Model (Clustering)

In the Add Model dialog box, you can add models to the Clustering node.

In the Add Model dialog box:

1. In the Algorithm field, select an algorithm, either KM, OC or EM.

- k-Means

- Orthogonal Partitioning Clustering

- Expectation Maximization. For this option, Oracle Database 12c Release 12.1 or later is required.

2. In the Name field, a default name is displayed. You can use the default name or rename the model.

3. In the Comment field, enter comments, if any. This is an optional comment.

4. Click OK.

This adds the model to the node.

Partition

In the Partition tab, you can build partitioned models.

- In the Maximum Number of Partitions field, set a value by clicking the arrows. This sets the partition cutoff value. The cutoff value must be greater than zero. If this option is not selected, then the native Oracle Data Miner cutoff value is used, which can be a very large value.

- Click Advanced Settings to set and select the type of partition build.

- To add columns for partitioning, click .

Note:

Only NUMBER and VARCHAR2 columns can be selected as partition columns. Case ID and Target columns cannot be selected as partition columns.

- To remove a partitioning column, select the columns and click .

- To move a column to the top, click .

- To move a column up, click

- To move a column down, click

- To move a column to the bottom, click

Sampling

The settings in the Sampling tab are applied to all models in the node. In the Sampling tab, you can specify the row size.

By default, Sampling is set to OFF. To set it to ON:

1. Click ON, and then select:

- System Determined

- User Specified and specify the row size

2. Click OK.

Input

The Input tab specifies the input for model build.

Determine inputs automatically (using heuristics) is selected by default for all models. Oracle Data Miner decides which attributes to use for input. For example, attributes that are almost constant may not be suitable for input. Oracle Data Miner also determines mining type and specifies that auto data preparation is performed for all attributes.

Note:
For R Build nodes, Auto Data Preparation is not performed.

After the node runs, rules are displayed explaining the heuristics. Click Show for detailed information.

You can change these selections. To do so, deselect Determine inputs automatically (using heuristics).

Related Topics

- Data Used for Model Building

Text

Text is available for any of the following data types: CHAR, VARCHAR2, BLOB, CLOB, NCHAR, or NVARCHAR2.

If you are connected to Oracle Database 12c Release 1 (12.1) and later, then you can specify text characteristics in the Text tab in the Edit Model Build dialog box.

If you specify text characteristics in the Text tab, then you are not required to use the Text nodes.

Note:

If you are connected to Oracle Database 11g Release 2 (11.2) or earlier, then use Text nodes. The Text tab is not available in Oracle Database 11g Release 2 and earlier.

To examine or specify text characteristics for data mining, either double-click the build node or right-click the node and select Edit from the context menu. Click the Text tab.

The Text tab enables you to modify the following:

- Categorical cutoff value: Enables you to control the cutoff used to determine whether a column should be considered as a Text or Categorical mining type. The cutoff value is an integer. It must be 10 or greater and less than or equal to 4000. The default value is 200.

- Default Transform Type: Specifies the default transformation type for column-level text settings. The values are:

- Token (Default): For Token as the transform type, the Default Settings are:

- Languages: Specifies the languages used in the documents. The default is English. To change this value, select an option from the drop-down list. You can select more than one language.

- Bigram: Select this option to mix the NORMAL token type with their bigram. For example, New York. The token type is BIGRAM.

- Stemming: By default, this option is not selected. Not all languages support stemming. If the language selected is English, Dutch, French, German, Italian, or Spanish, then stemming is automatically enabled. If Stemming is enabled, then stemmed words are returned for supported languages. Otherwise the original words are returned.

Note:

If both Bigram and Stemming are selected, then the token type is STEM_BIGRAM. If neither Bigram nor Stemming is selected, then token type is NORMAL.

- Stoplists: Specifies the stoplist to use. The default setting is to use the default stoplist. You can add stoplists or edit stoplists. If you select more than one language, and the selected stoplist is Default, then the default stop words for languages are added to the default stoplist from the repository. No duplicate stop words are added.

- Click to view the Stoplist Details. This opens the Stoplist Details dialog box.

- Click to add a new stoplist. This opens the New Stoplist Wizard.

- Tokens: Specify the following:

- Max number of tokens across all rows (document). The default is 3000.

- Min number of rows (document) required for a token

- Theme: If Theme is selected, then the Default Settings are as follows:

- Language: Specifies the languages used in the documents. The default is English. To change this value, select one from the drop-down list. You can select more than one language.

- Stoplists: Specifies the stoplist to use. The default setting is to use the default stoplist. You can add stoplists or edit stoplists. If you select more than one language and the selected stoplist is Default, then the default stop words for languages are added to the default stoplist (from the repository). No duplicate stop words are added.

- Themes: Specifies the maximum number of themes across all documents. The default is 3000.

- Synonym: The Synonym tab is enabled only if a thesaurus is loaded. By default, no thesaurus is loaded. You must manually load the default thesaurus provided by Oracle Text or upload your own thesaurus.

- Click Stoplists to open the Stoplist Editor. You can view, edit, and create stoplists.
You can use the same stoplist for all text columns.

Related Topics

- Oracle Text Concepts

- Text Nodes

- Stoplist Editor

Advanced Settings for Clustering Models

In the Advanced Settings dialog box, you can review and change settings related to data usage and algorithms used in the model.

To access advanced settings, click in the Edit Clustering Build Node dialog box. Alternately, right-click the node and select Advanced Settings. The Advanced Settings dialog box list all the models in the upper pane.

You can perform the following tasks:

- Inspect and change the data usage and algorithm

- Add models to the node

- Delete models from the node

In the lower pane, you can view and modify data usage and algorithm settings for the model selected in the upper pane. You can edit the following:

- Data Usage

- Algorithm Settings

The settings that can be changed depend on the algorithms.

Related Topics

- Advanced Settings Overview

- KM Algorithm Settings

- OC Algorithm Settings

- EM Algorithm Settings

Clustering Build Node Properties

In the Properties pane, you can examine and change the characteristics or properties of a node.

To view the properties of a node, click the node and click Properties. If the Properties pane is closed, then go to View and click Properties. Alternately, right-click the node and click Go to Properties.

The Clustering Build node properties has these sections:

- Models
The Models section in Properties lists the models that are built when the nodes are run.

- Build
The Build section in Properties displays the Case ID of the Clustering model.

- Partition
In the Partition tab, you can build partitioned models.

- Sampling
The settings in the Sampling tab are applied to all models in the node. In the Sampling tab, you can specify the row size.

- Details
The Details section displays the name of the node and any comments about it.

Models

The Models section in Properties lists the models that are built when the nodes are run.

The default is to build two Clustering models using the KM, OC, and EM algorithms.

The Model Settings grid lists the models in the node. You can perform the following tasks:

- Search models

- Add models

- Delete models

- Duplicate models

- View models

- Indicate which models are passed on to subsequent nodes.

- Add Model (Clustering)
In the Add Model dialog box, you can add models to the Clustering node.

- View Models
Use the View Models option to view the details of the models that are built after running the workflow.

- Clustering Node Output Column
The Output column in the Model Settings grid controls the passing of models to subsequent nodes.

Add Model (Clustering)

In the Add Model dialog box, you can add models to the Clustering node.

In the Add Model dialog box:

1. In the Algorithm field, select an algorithm, either KM, OC or EM.

- k-Means

- Orthogonal Partitioning Clustering

- Expectation Maximization. For this option, Oracle Database 12c Release 12.1 or later is required.

2. In the Name field, a default name is displayed. You can use the default name or rename the model.

3. In the Comment field, enter comments, if any. This is an optional comment.

4. Click OK.

This adds the model to the node.

View Models

Use the View Models option to view the details of the models that are built after running the workflow.

To view models, you must select a model from the list to open the model viewer. A model must be built successfully before it can be viewed.

Clustering Node Output Column

The Output column in the Model Settings grid controls the passing of models to subsequent nodes.

By default, all models are passed to subsequent nodes.

- To ignore a model, that is, to not pass it to subsequent nodes, click . The Output icon changes to .

- To cancel the ignore, click the Ignore icon again. The icon changes to the Output icon.

Build

The Build section in Properties displays the Case ID of the Clustering model.

To change the case ID, select an attribute from the Case ID drop-down list.

Partition

In the Partition tab, you can build partitioned models.

- In the Maximum Number of Partitions field, set a value by clicking the arrows. This sets the partition cutoff value. The cutoff value must be greater than zero. If this option is not selected, then the native Oracle Data Miner cutoff value is used, which can be a very large value.

- Click Advanced Settings to set and select the type of partition build.

- To add columns for partitioning, click .

Note:

Only NUMBER and VARCHAR2 columns can be selected as partition columns. Case ID and Target columns cannot be selected as partition columns.

- To remove a partitioning column, select the columns and click .

- To move a column to the top, click .

- To move a column up, click

- To move a column down, click

- To move a column to the bottom, click

Sampling

The settings in the Sampling tab are applied to all models in the node. In the Sampling tab, you can specify the row size.

By default, Sampling is set to OFF. To set it to ON:

1. Click ON, and then select:

- System Determined

- User Specified and specify the row size

2. Click OK.

Details

The Details section displays the name of the node and any comments about it.

You can change the name and comments in the fields here:

- Node Name

- Node Comments

Clustering Build Node Context Menu

The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

To view the context menu options, right click the node. The following options are available in the context menu:

- Connect

- Edit. Opens the Edit Association Build Node dialog box.

- Validate Parents

- Advanced Settings. Opens the Advanced Settings for Association Node dialog box.

- Run

- View Models. Opens the appropriate viewer (KM Model Viewer or OC Model Viewer) for the selected model.

- Generate Apply Chain

- Cut

- Copy

- Paste

- Extended Paste

- Select All

- Performance Settings. This opens the Edit Selected Node Settings dialog box, where you can set Parallel Settings and In-Memory settings for the node.

- Show Event Log. Displayed only if the running of the node fails.

- Navigate

Related Topics

- Edit Association Build Node

- Advanced Settings for Association Node

Explicit Feature Extraction Node

The Explicit Feature Extraction node is built using the feature extraction algorithm called Explicit Semantic Analysis (ESA).

ESA is a vectorial representation of text, which can be individual words or entire documents. The algorithm uses a document corpus as the knowledge base. In ESA, a word is represented as a column vector in the tf–idf matrix of the text corpus and a document is represented as the centroid of the vectors representing its words. Oracle Data Mining provides a prebuilt ESA model based on Wikipedia. You can import the model to Oracle Data Miner for mining purposes.

You can use the Explicit Feature Extraction node for the following purposes:

- Document classification

- Calculations related to semantics

- Information retrieval

- Create Explicit Feature Extraction Node
You create an Explicit Feature Extraction node for the purposes related to information retrieval, documentation classification, and for all other calculations related to semantics.

- Edit Explicit Feature Extraction Node
When you create an Explicit Feature Extraction node, an ESA model with the default algorithm settings is added. You can add additional ESA models and edit them in the Edit Explicit Feature Extraction Node dialog box.

- Advanced Model Settings
In the Advanced Model Settings dialog box, you can edit and set algorithm settings of the selected Explicit Semantic Analysis model.

- Explicit Feature Extraction Build Properties
In the Properties pane, you can examine and change the characteristics or properties of a node.

- Explicit Feature Extraction Context Menu
The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

Related Topics

- About Oracle Database In-Memory

- About Parallel Processing

Create Explicit Feature Extraction Node

You create an Explicit Feature Extraction node for the purposes related to information retrieval, documentation classification, and for all other calculations related to semantics.

First create a workflow and then identify or create a Data Source node.

The input for an Explicit Feature Extraction node is any node . To create an Explicit Feature Extraction node:

1. In the Components pane, go to Workflow Editor. If the Components pane is not visible, then in the SQL Developer menu bar, go to View and click Components. Alternately, press Ctrl+Shift+P to dock the Components pane.

2. In the Workflow Editor, expand Models and click Explicit Feature Extraction.

3. Drag and drop the node from the Components pane to the Workflow pane.
The node is added to the workflow. The GUI shows that the node has no data associated with it. Therefore, it cannot be run.

4. Move to the node that provides data for the build. Right-click and click Connect. Drag the line to the Explicit Feature Extraction. node and click again.

5. You can also specify a case ID, edit the data usage, and change the algorithm settings. To perform any of these tasks, right-click the node and select Edit. The Edit Explicit Feature Extraction Node dialog box opens.

6. The node is now ready to build. Right-click the node and click Run.

Related Topics

- Edit Explicit Feature Extraction Node

Edit Explicit Feature Extraction Node

When you create an Explicit Feature Extraction node, an ESA model with the default algorithm settings is added. You can add additional ESA models and edit them in the Edit Explicit Feature Extraction Node dialog box.

The Edit Explicit Feature Extraction Node dialog box comprises the following tabs:

- Build
The Build tab enables you to specify or change the characteristics of the models to build.

- Partition
In the Partition tab, you can build partitioned models.

- Sampling
The settings in the Sampling tab are applied to all models in the node. In the Sampling tab, you can specify the row size.

- Input
The Input tab specifies the input for model build.

- Text
Text is available for any of the following data types: CHAR, VARCHAR2, BLOB, CLOB, NCHAR, or NVARCHAR2.

Related Topics

- Advanced Model Settings

Build

The Build tab enables you to specify or change the characteristics of the models to build.

To edit the characteristics of the model to build, follow these steps:

1. In the Topic ID field, select an attribute for building the model.

2. In the Model Settings list, select which models you want to build. You can build Support Vector Machine (SVM) and Generalized Linear Models (GLM). You can delete any of these models by selecting the model and clicking

- To delete any model, select the model and click .

- To add models, click .

- To edit a model, click .

- To copy an existing model, select the model and click

3. Click OK.

- Add Model
The Add Model dialog box allows you to add additional ESA models to the Explicit Feature Extraction node.

Related Topics

- Advanced Settings Overview

Add Model

The Add Model dialog box allows you to add additional ESA models to the Explicit Feature Extraction node.

To add a model:

1. In the Algorithm field, the Explicit Semantic Algorithm is displayed.

2. In the Name field, edit the name.

3. In the Commentsfield enter comments, if any.

4. Click OK.

Partition

In the Partition tab, you can build partitioned models.

- In the Maximum Number of Partitions field, set a value by clicking the arrows. This sets the partition cutoff value. The cutoff value must be greater than zero. If this option is not selected, then the native Oracle Data Miner cutoff value is used, which can be a very large value.

- Click Advanced Settings to set and select the type of partition build.

- To add columns for partitioning, click .

Note:

Only NUMBER and VARCHAR2 columns can be selected as partition columns. Case ID and Target columns cannot be selected as partition columns.

- To remove a partitioning column, select the columns and click .

- To move a column to the top, click .

- To move a column up, click

- To move a column down, click

- To move a column to the bottom, click

- Add Partitioning Columns
Partitioning columns result in building a virtual model for each unique partition. Because the virtual model uses data only from a specific partition, it can potentially predict cases more accurately than if you did not select a partition.

Add Partitioning Columns

Partitioning columns result in building a virtual model for each unique partition. Because the virtual model uses data only from a specific partition, it can potentially predict cases more accurately than if you did not select a partition.

In addition to selecting attributes, you can specify partitioning expressions. Partitioning expressions are concatenated and the result expression is the same for all predictive functions.

1. Select one or more attributes in the Available Attributes list to serve as partitions.

2. Move the selected columns to the Selected Attributes list using the arrows.

3. Click OK. The attributes are moved to the Partition list.

Optionally, you can add partitioning expressions.

Sampling

The settings in the Sampling tab are applied to all models in the node. In the Sampling tab, you can specify the row size.

By default, Sampling is set to OFF. To set it to ON:

1. Click ON, and then select:

- System Determined

- User Specified and specify the row size

2. Click OK.

Input

The Input tab specifies the input for model build.

Determine inputs automatically (using heuristics) is selected by default for all models. Oracle Data Miner decides which attributes to use for input. For example, attributes that are almost constant may not be suitable for input. Oracle Data Miner also determines mining type and specifies that auto data preparation is performed for all attributes.

Note:
For R Build nodes, Auto Data Preparation is not performed.

After the node runs, rules are displayed explaining the heuristics. Click Show for detailed information.

You can change these selections. To do so, deselect Determine inputs automatically (using heuristics).

Related Topics

- Data Used for Model Building

Text

Text is available for any of the following data types: CHAR, VARCHAR2, BLOB, CLOB, NCHAR, or NVARCHAR2.

If you are connected to Oracle Database 12c Release 1 (12.1) and later, then you can specify text characteristics in the Text tab in the Edit Model Build dialog box.

If you specify text characteristics in the Text tab, then you are not required to use the Text nodes.

Note:

If you are connected to Oracle Database 11g Release 2 (11.2) or earlier, then use Text nodes. The Text tab is not available in Oracle Database 11g Release 2 and earlier.

To examine or specify text characteristics for data mining, either double-click the build node or right-click the node and select Edit from the context menu. Click the Text tab.

The Text tab enables you to modify the following:

- Categorical cutoff value: Enables you to control the cutoff used to determine whether a column should be considered as a Text or Categorical mining type. The cutoff value is an integer. It must be 10 or greater and less than or equal to 4000. The default value is 200.

- Default Transform Type: Specifies the default transformation type for column-level text settings. The values are:

- Token (Default): For Token as the transform type, the Default Settings are:

- Languages: Specifies the languages used in the documents. The default is English. To change this value, select an option from the drop-down list. You can select more than one language.

- Bigram: Select this option to mix the NORMAL token type with their bigram. For example, New York. The token type is BIGRAM.

- Stemming: By default, this option is not selected. Not all languages support stemming. If the language selected is English, Dutch, French, German, Italian, or Spanish, then stemming is automatically enabled. If Stemming is enabled, then stemmed words are returned for supported languages. Otherwise the original words are returned.

Note:

If both Bigram and Stemming are selected, then the token type is STEM_BIGRAM. If neither Bigram nor Stemming is selected, then token type is NORMAL.

- Stoplists: Specifies the stoplist to use. The default setting is to use the default stoplist. You can add stoplists or edit stoplists. If you select more than one language, and the selected stoplist is Default, then the default stop words for languages are added to the default stoplist from the repository. No duplicate stop words are added.

- Click to view the Stoplist Details. This opens the Stoplist Details dialog box.

- Click to add a new stoplist. This opens the New Stoplist Wizard.

- Tokens: Specify the following:

- Max number of tokens across all rows (document). The default is 3000.

- Min number of rows (document) required for a token

- Theme: If Theme is selected, then the Default Settings are as follows:

- Language: Specifies the languages used in the documents. The default is English. To change this value, select one from the drop-down list. You can select more than one language.

- Stoplists: Specifies the stoplist to use. The default setting is to use the default stoplist. You can add stoplists or edit stoplists. If you select more than one language and the selected stoplist is Default, then the default stop words for languages are added to the default stoplist (from the repository). No duplicate stop words are added.

- Themes: Specifies the maximum number of themes across all documents. The default is 3000.

- Synonym: The Synonym tab is enabled only if a thesaurus is loaded. By default, no thesaurus is loaded. You must manually load the default thesaurus provided by Oracle Text or upload your own thesaurus.

- Click Stoplists to open the Stoplist Editor. You can view, edit, and create stoplists.
You can use the same stoplist for all text columns.

Related Topics

- Oracle Text Concepts

- Text Nodes

- Stoplist Editor

Advanced Model Settings

In the Advanced Model Settings dialog box, you can edit and set algorithm settings of the selected Explicit Semantic Analysis model.

The Explicit Semantic Algorithm (ESA) model has only three algorithm settings:

- Data Usage: Displays the attribute name, data type, mining type and other details about the attributes in the selected model. You can customize your input source here.

- Algorithm Settings: The following are the algorithm settings for an ESA model:

- Top N Features: Controls the maximum number of features per attribute. It must be a positive integer. The default is 1000.

- Minimum Items: Determines the minimum number of non-zero entries that need to be present in an input row.

- Threshold Value: This setting thresholds very small values in the transformed build data. It must be a non-negative number. The default is 0.00000001.

Explicit Feature Extraction Build Properties

In the Properties pane, you can examine and change the characteristics or properties of a node.

To view the properties of a node, click the node and click Properties. If the Properties pane is closed, then go to View and click Properties. Alternately, right-click the node and click Go to Properties.

The Explicit Feature Extraction Build node properties has these sections:

- Models
The Models section displays a list of the models defined in the node. By default, one model is built for each algorithm supported by the node.

- Build
The Build section displays information related to the model build. For models that have a target, such as Classification and Regression, the targets are listed. All models in a node have the same target.

- Partition
In the Partition tab, you can build partitioned models.

- Details
The Details section displays the node name and comments about the node.

Models

The Models section displays a list of the models defined in the node. By default, one model is built for each algorithm supported by the node.

For each model, the name of the model, build information, the algorithm, and comments are listed in a grid. The Build column shows the time and date of the last successful build or if the model is not built or did not build successfully.

You can add, delete, or view models in the list. You can also indicate in which models are passed to subsequent nodes or not.

- To delete a model from the list, select it and click .

- To add a model, click . The Add Model dialog box opens.

- To view a model that was built successfully, select the model and click .

You can tune classification models from Properties pane.

Related Topics

- Tuning Classification Models

Build

The Build section displays information related to the model build. For models that have a target, such as Classification and Regression, the targets are listed. All models in a node have the same target.

The Build section displays the following:

- Target: Displays the target. To change the target, select a new target from the drop-down list.

- Case ID: Displays the case ID of the model defined in this node. All the models in the node have the same case IDs. To edit the case IDs, select a different case ID from the drop-down list.

- Transaction ID: Displayed for Association models only. To change the transaction ID, click Edit.

- Item ID: Displayed for Association models only. To change the value, select an option from the drop-down list.

- Item Value: Displayed for Association models only. To change the value, select an option from the drop-down list.

Partition

In the Partition tab, you can build partitioned models.

- In the Maximum Number of Partitions field, set a value by clicking the arrows. This sets the partition cutoff value. The cutoff value must be greater than zero. If this option is not selected, then the native Oracle Data Miner cutoff value is used, which can be a very large value.

- Click Advanced Settings to set and select the type of partition build.

- To add columns for partitioning, click .

Note:

Only NUMBER and VARCHAR2 columns can be selected as partition columns. Case ID and Target columns cannot be selected as partition columns.

- To remove a partitioning column, select the columns and click .

- To move a column to the top, click .

- To move a column up, click

- To move a column down, click

- To move a column to the bottom, click

Details

The Details section displays the node name and comments about the node.

You can change the name of the node and edit the comments in this section. The new node name and comments must meet the requirements.

Related Topics

- Node Name and Node Comments

Explicit Feature Extraction Context Menu

The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

To view the context menu options, right click the node. The following options are available in the context menu:

- Connect

- Run

- Force Run

- Create Schedule

- Edit. Opens the Edit Explicit Feature Extraction Node dialog box.

- Advanced Settings. Opens the Advanced Model Settings dialog box.

- View Models. Opens the ESA Model Viewer.

- Generate Apply Chain

- Show Event Log

- Deploy

- Cut

- Copy

- Paste

- Extended Paste

- Select All

- Performance Settings. This opens the Edit Selected Node Settings dialog box, where you can set Parallel Settings and In-Memory settings for the node.

- Copy Image to Clipboard

- Save Image as. Opens the Publish Diagram dialog box.

- Go to Properties

- Navigate

Related Topics

- ESA Model Viewer

- Publish Diagram

- Advanced Model Settings

- Edit Explicit Feature Extraction Node

Feature Extraction Node

A Feature Extraction node uses the Nonnegative Matrix Factorization (NMF) algorithm, to build models.

There are two ways to extract features:

- Build a feature extraction model, using a Feature Extraction node.

- Use a Feature Extraction Query, which is one of the predictive queries.

If Oracle Data Miner is connected to Oracle Database 12c Release 1 (12.1) and later, then the Feature Extraction node uses PCA and SVD algorithms to build models.

Note:

Principal Components Analysis and Singular Value Decomposition models require Oracle Database 12c Release 1 (12.1) and later.

A Feature Extraction Build can run in parallel.

This section contains the following topics:

- Default Behavior of Feature Extraction Node
By default, a Feature Extraction node builds one model using the Non-Negative Matrix Factorization (NMF) algorithm.

- Create Feature Extraction Node
You create a Feature Extraction node to build feature extraction models. The node uses the Nonnegative Matrix Factorization (NMF) algorithm.

- Data for Model Build
Oracle Data Miner uses heuristic techniques on data for model build.

- Edit Feature Extraction Build Node
In the Edit Feature Extraction Build Node dialog box, you can specify or change the characteristics of the models to build.

- Advanced Settings for Feature Extraction
The options in Advanced Settings for Feature Extraction allows you to inspect and change the data usage and algorithm settings for each model in the node.

- Feature Extraction Node Properties
In the Properties pane, you can examine and change the characteristics or properties of a node.

- Feature Extraction Node Context Menu
The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

Related Topics

- About Parallel Processing

- About Oracle Database In-Memory

- Feature Extraction Query

Default Behavior of Feature Extraction Node

By default, a Feature Extraction node builds one model using the Non-Negative Matrix Factorization (NMF) algorithm.

If you are connected to Oracle Database 12c or later, then the node builds two models by default:

- NMF model

- PCA model

You can add SVD models.

All models in the node use the same build data and have the same case ID, if you specify a case ID.

Related Topics

- Nonnegative Matrix Factorization

- Singular Value Decomposition and Principal Components Analysis

Create Feature Extraction Node

You create a Feature Extraction node to build feature extraction models. The node uses the Nonnegative Matrix Factorization (NMF) algorithm.

First create a workflow. Then, identify or create a Data Source node.

To create a Feature Extraction node:

1. In the Components pane, go to Workflow Editor. If the Components pane is not visible, then in the SQL Developer menu bar, go to View and click Components. Alternately, press Ctrl+Shift+P to dock the Components pane.

2. In the Workflow Editor expand Models, and click Feature Extraction.

3. Drag and drop the node from the Components pane to the Workflow pane.
The node is added to the workflow. The GUI shows that the node has no data associated with it. Therefore, it cannot be run.

4. Move to the node that provides data for the build. Right-click and click Connect. Drag the line to the Feature Extraction node and click again.

5. You can edit the node. To edit the node, right-click the node and click Edit. The Edit Feature Extraction Build Node dialog box opens.

6. The node is ready to build. Right-click the node and click Run.

Related Topics

- Edit Feature Extraction Build Node

Data for Model Build

Oracle Data Miner uses heuristic techniques on data for model build.

Oracle Data Miner uses heuristics to:

- Determine the attributes of the input data used for model build.

- Determine the mining type of each attribute.

Related Topics

- Data Used for Model Building

Edit Feature Extraction Build Node

In the Edit Feature Extraction Build Node dialog box, you can specify or change the characteristics of the models to build.

To edit a Feature Build node, either double-click a Feature Build node, or right-click the node and select Edit. The Edit Feature Extraction Build Node dialog box opens. The same dialog box opens when you drop a Feature Build node on a workflow.

The Edit Feature Extraction Build dialog box has three tabs:

- Build (Feature Extraction)
In the Build tab, you can edit settings related to the Feature Extraction build node.

- Partition
In the Partition tab, you can build partitioned models.

- Sampling
The settings in the Sampling tab are applied to all models in the node. In the Sampling tab, you can specify the row size.

- Input
The Input tab specifies the input for model build.

- Text
Text is available for any of the following data types: CHAR, VARCHAR2, BLOB, CLOB, NCHAR, or NVARCHAR2.

Related Topics

- Viewing and Changing Data Usage

Build (Feature Extraction)

In the Build tab, you can edit settings related to the Feature Extraction build node.

You can perform the following tasks:

- Case ID: Specify case ID for Feature Extraction is optional. Specify one by selecting an attribute from the drop-down list.

- Add Model: To add a model, click

- Delete: To delete a model, select the model and click .

- Copy: To copy an existing model, select the model and click .

- Add Model (Feature Extraction)
In the Add Model dialog box, you can add additional models.

Add Model (Feature Extraction)

In the Add Model dialog box, you can add additional models.

To add a model, click .

1. In the Algorithm field, select an algorithm. The default algorithm is NMF.

2. In the Name field, the default name is displayed. You can accept the default name or change it.

3. In the Comments field, enter comments, if any. This is an optional field.

4. Click OK. The model is added to the list. The new model has the same build characteristics as existing models. The new model has the default values for advanced settings.

Partition

In the Partition tab, you can build partitioned models.

- In the Maximum Number of Partitions field, set a value by clicking the arrows. This sets the partition cutoff value. The cutoff value must be greater than zero. If this option is not selected, then the native Oracle Data Miner cutoff value is used, which can be a very large value.

- Click Advanced Settings to set and select the type of partition build.

- To add columns for partitioning, click .

Note:

Only NUMBER and VARCHAR2 columns can be selected as partition columns. Case ID and Target columns cannot be selected as partition columns.

- To remove a partitioning column, select the columns and click .

- To move a column to the top, click .

- To move a column up, click

- To move a column down, click

- To move a column to the bottom, click

Sampling

The settings in the Sampling tab are applied to all models in the node. In the Sampling tab, you can specify the row size.

By default, Sampling is set to OFF. To set it to ON:

1. Click ON, and then select:

- System Determined

- User Specified and specify the row size

2. Click OK.

Input

The Input tab specifies the input for model build.

Determine inputs automatically (using heuristics) is selected by default for all models. Oracle Data Miner decides which attributes to use for input. For example, attributes that are almost constant may not be suitable for input. Oracle Data Miner also determines mining type and specifies that auto data preparation is performed for all attributes.

Note:
For R Build nodes, Auto Data Preparation is not performed.

After the node runs, rules are displayed explaining the heuristics. Click Show for detailed information.

You can change these selections. To do so, deselect Determine inputs automatically (using heuristics).

Related Topics

- Data Used for Model Building

Text

Text is available for any of the following data types: CHAR, VARCHAR2, BLOB, CLOB, NCHAR, or NVARCHAR2.

If you are connected to Oracle Database 12c Release 1 (12.1) and later, then you can specify text characteristics in the Text tab in the Edit Model Build dialog box.

If you specify text characteristics in the Text tab, then you are not required to use the Text nodes.

Note:

If you are connected to Oracle Database 11g Release 2 (11.2) or earlier, then use Text nodes. The Text tab is not available in Oracle Database 11g Release 2 and earlier.

To examine or specify text characteristics for data mining, either double-click the build node or right-click the node and select Edit from the context menu. Click the Text tab.

The Text tab enables you to modify the following:

- Categorical cutoff value: Enables you to control the cutoff used to determine whether a column should be considered as a Text or Categorical mining type. The cutoff value is an integer. It must be 10 or greater and less than or equal to 4000. The default value is 200.

- Default Transform Type: Specifies the default transformation type for column-level text settings. The values are:

- Token (Default): For Token as the transform type, the Default Settings are:

- Languages: Specifies the languages used in the documents. The default is English. To change this value, select an option from the drop-down list. You can select more than one language.

- Bigram: Select this option to mix the NORMAL token type with their bigram. For example, New York. The token type is BIGRAM.

- Stemming: By default, this option is not selected. Not all languages support stemming. If the language selected is English, Dutch, French, German, Italian, or Spanish, then stemming is automatically enabled. If Stemming is enabled, then stemmed words are returned for supported languages. Otherwise the original words are returned.

Note:

If both Bigram and Stemming are selected, then the token type is STEM_BIGRAM. If neither Bigram nor Stemming is selected, then token type is NORMAL.

- Stoplists: Specifies the stoplist to use. The default setting is to use the default stoplist. You can add stoplists or edit stoplists. If you select more than one language, and the selected stoplist is Default, then the default stop words for languages are added to the default stoplist from the repository. No duplicate stop words are added.

- Click to view the Stoplist Details. This opens the Stoplist Details dialog box.

- Click to add a new stoplist. This opens the New Stoplist Wizard.

- Tokens: Specify the following:

- Max number of tokens across all rows (document). The default is 3000.

- Min number of rows (document) required for a token

- Theme: If Theme is selected, then the Default Settings are as follows:

- Language: Specifies the languages used in the documents. The default is English. To change this value, select one from the drop-down list. You can select more than one language.

- Stoplists: Specifies the stoplist to use. The default setting is to use the default stoplist. You can add stoplists or edit stoplists. If you select more than one language and the selected stoplist is Default, then the default stop words for languages are added to the default stoplist (from the repository). No duplicate stop words are added.

- Themes: Specifies the maximum number of themes across all documents. The default is 3000.

- Synonym: The Synonym tab is enabled only if a thesaurus is loaded. By default, no thesaurus is loaded. You must manually load the default thesaurus provided by Oracle Text or upload your own thesaurus.

- Click Stoplists to open the Stoplist Editor. You can view, edit, and create stoplists.
You can use the same stoplist for all text columns.

- Stoplist Details
The Stoplist Details dialog box lists the stopwords and stopthemes for the selected Stoplist. You can also add and delete stopwords and stopthemes.

- Add Stopwords Stopthemes from Features
The Add Stopwords/Stopthemes from Features dialog box allows you to select stopwords or stopthemes from the generated features to be included as new stopwords or stopthemes for the selected stoplist.

Related Topics

- Oracle Text Concepts

- Text Nodes

- Stoplist Editor

Stoplist Details

The Stoplist Details dialog box lists the stopwords and stopthemes for the selected Stoplist. You can also add and delete stopwords and stopthemes.

To view the stoplist details, you must select the stoplist first in the Stoplist drop-down list.

1. To view stopwords and stopthemes:

- Click to view the stopwords in the stoplist.

- Click to view the stopthemes in the stoplist.

2. To add stopwords and stopthemes from features, click .
This opens the Add Stopwords/Stopthemes from Features dialog box.

3. To add and delete stopwords:

- Click to add stopwords or stopthemes to the stoplist. This opens the Add Stopwords/Stopthemes dialog box, where you can type the stopwords or stopthemes separated by comma to add them in the stoplist.

- Click to delete the selected stopword from stoplist.

4. Click OK.

Add Stopwords Stopthemes from Features

The Add Stopwords/Stopthemes from Features dialog box allows you to select stopwords or stopthemes from the generated features to be included as new stopwords or stopthemes for the selected stoplist.

To include a stopword or stoptheme:

1. Select the stopwords or stopthemes that you want to add.

2. Click OK.

Advanced Settings for Feature Extraction

The options in Advanced Settings for Feature Extraction allows you to inspect and change the data usage and algorithm settings for each model in the node.

You can perform the following:

- Inspect and change data usage.

- Change algorithm settings for each model in the node.

To change or view advanced settings, click in the Edit Feature Extraction Build Node dialog box. Alternately, right-click the node and select Advanced Settings. The advanced settings selection enables you to inspect and change the data usage and algorithm settings for each model in the node.

In the upper pane, all models are listed. You can perform the following tasks:

- Delete: To delete a model, select it and click .

- Add: To add a model, click .

In the lower pane, you can view or edit the following for the model selected in the upper pane:

- Data Usage

- Algorithm Settings
The settings depend on the algorithm:

- NMF Algorithm Settings

- PCA Algorithm Settings

- SVD Algorithm Settings

PCA and SVD are available if Oracle Data Miner is connected to Oracle Database 12c Release 1 (12.1) or later.

Related Topics

- Advanced Settings Overview

- Edit Feature Extraction Build Node

Feature Extraction Node Properties

In the Properties pane, you can examine and change the characteristics or properties of a node.

To view the properties of a node, click the node and click Properties. If the Properties pane is closed, then go to View and click Properties. Alternately, right-click the node and click Go to Properties.

The Feature Extraction node Properties pane has the following sections:

- Models: Displays the details of model settings. You can edit models here.

- Build: Displays the case ID for the models defined in this node. All the models in the node have the same case ID. To edit the case ID, select a different attribute from the Case ID list.

- Partition: Displays the details related to partitioned models. You can add and modify partitioned models here.

- Details: Displays the details related to the Feature Extraction Build node.

Feature Extraction Node Context Menu

The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

To view the context menu options, right click the node. The following options are available in the context menu:

- Connect

- Edit: Opens the Edit Feature Extraction Build Node dialog box.

- Advanced Settings: Opens the Advanced Settings for Feature Extraction dialog box.

- Validate Parents

- Run

- Force Run

- View Models: Opens the NMF Model Viewer for the selected model.

- Generate Apply Chain

- Cut

- Copy

- Paste

- Extended Paste

- Select All

- Performance Settings. This opens the Edit Selected Node Settings dialog box, where you can set Parallel Settings and In-Memory settings for the node.

- Show Event Log

- Show Runtime Errors. Displayed only if there is an error.

- Show Validation Errors. Displayed only if there are validation errors.

- Navigate

Related Topics

- Edit Feature Extraction Build Node

- Advanced Settings for Feature Extraction

Model Node

Model nodes rely on database resources for their definition. It may be necessary to refresh a node definition if the database resources change.

For example, if the resources are deleted or re-created. You can specify a model that was built using either of the ODM APIs. The models in a Model node must satisfy the model constraints.

The Model node takes no input. A Model node can be an input to any node that accepts models, such as the Apply and Test nodes, at least for some function types. For example, if a model node contains Classification or Regression models, then it can be input to a test node. Test data must be prepared in the same way that the build data was prepared.

- Create a Model Node
A Model node enables you to add models to a workflow that were not built in the workflow.

- Edit Model Selection
In the Edit Model Section dialog box, you can select one or more models to include in the Model node or to remove models from the Model node.

- Model Node Properties
In the Properties pane, you can examine and change the characteristics or properties of a node.

- Model Node Context Menu
The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

Related Topics

- About Parallel Processing

- About Oracle Database In-Memory

- Model Constraints

- Refresh Nodes

Create a Model Node

A Model node enables you to add models to a workflow that were not built in the workflow.

To add a model node to a workflow and add models to the model node:

1. In the Components pane, go to Workflow Editor. If the Components pane is not visible, then in the SQL Developer menu bar, go to View and click Components. Alternately, press Ctrl+Shift+P to dock the Components pane.

2. In the Workflow Editor, expand Models, and click Model.

3. Drag and drop the node from the Components pane to the Workflow pane.
The node is added to the workflow. The GUI shows that the node has no data associated with it. Therefore, it cannot be run.

4. The Edit Model Selection dialog box opens automatically. The models in the Model node must have the same mining function and the same target (Classification and Regression models only).

Related Topics

- Edit Model Selection

Edit Model Selection

In the Edit Model Section dialog box, you can select one or more models to include in the Model node or to remove models from the Model node.

To edit the models in the node, double-click the Model node or right-click the Model node and select Edit.

Note:

All the models in a model node must satisfy the model constraints.

You can perform the following tasks:

- Select models from the Available Compatible Models list and move them to the Selected Models list using the controls between the lists. The selected models are checked for compatibility. The models in a model node must satisfy the model constraints. The selected models are part of the model node. You can view the models using the Model node properties.

- Include models from other schemas. To include models, select Include Models from Other Schemas.

- Filter the Available Compatible Models list in the following ways:

- Select a model function from the Model Function list. The options are:

- All

- Anomaly Detection

- Association Rules

- Regression

- Clustering

- Feature Extraction

- Sort the models by name, function, algorithm, target, target data type, creation date, or comments. To sort, click the column header in the list of available models.

- Add and remove models:

- Add models by moving them from Available Compatible Models list to the Selected Models list.

- Remove models by moving them from the Selected Modes list to the Available Compatible Models list. You can also remove models using the Models tab.

- Model Constraints

Model Constraints

A Model node consists of models that are similar. The models in a Model node must satisfy the following;

- All models must have the same function type (Classification, Regression, Clustering, Anomaly Detection, Association Rules, or Feature Extraction). You cannot include models that have different function types.
You can add models that are built using different algorithms if the models have the same function type.

- Classification or Regression models must have the same target attribute. The target attributes must all have the same data type.
CHAR and VARCHAR2 are considered to be the same data type for Classification models.

- Classification models must have the same list of target values.

Model Node Properties

In the Properties pane, you can examine and change the characteristics or properties of a node.

To view the properties of a node, click the node and click Properties. If the Properties pane is closed, then go to View and click Properties. Alternately, right-click the node and click Go to Properties.

In the Model node Properties pane, you can:

- Add models to the Model node

- Delete models from the Model node

- View models in the Model node

The Properties pane for a model node source node has the following sections:

- Models (Model Node)
The Models section shows the mining function that the models use and lists all the models included in the node in a grid.

- Details
The Details section displays the node name and comments about the node.

Models (Model Node)

The Models section shows the mining function that the models use and lists all the models included in the node in a grid.

You can search for models, add models to the node, and delete models. You can perform the following tasks:

- Add Models: To add models:

1. Click . The Edit Model Selection dialog box opens.

2. In the Edit Model Selection dialog box, select the models to add to the node. You can add models from other schemas too. However, any models that you add must be compatible with the models already in the node.

3. Click OK. This adds the models to the node. You can go to the Properties pane for the Model node to view the models.

- Delete Models: To delete a model, select it and click .

- View Models: To view a model, select it and click .

- Refresh models: To refresh models, click . If data on the server changes, then it may be necessary to refresh the node.

Related Topics

- Refresh Nodes

- Edit Model Selection

Details

The Details section displays the node name and comments about the node.

You can change the name of the node and edit the comments in this section. The new node name and comments must meet the requirements.

Related Topics

- Node Name and Node Comments

Model Node Context Menu

The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

The following options are available in the context menu:

- Connect

- Edit. Opens the Edit Model Selection dialog box.

- Run. Validates that the models specified in the node exist.

- View Models

- Generate Apply Chain

- Cut

- Copy

- Paste

- Extended Paste

- Select All

- Show Event Log

- Show Runtime Errors. Displayed only if there is an error.

- Show Validation Errors. Displayed only if there are validation errors.

- Navigate

Model Details Node

The Model Detail node extracts and provides information about the model and algorithms.

The Model Details nodes are the most useful for application developers. The Model Details node performs the following functions:

- Extracts model details from a Model Build node, a Model node or any node that outputs a model.

- Reveals information about model attributes and their treatment by the algorithm. The output depends on the type of models selected and the specific type of model details you specify.

- The output of the Model Details node is a data flow. To enable the data to persist, use a Create Table or View node.

A Model Details node can run in parallel.

This section on Model Detail node contains the following topics:

- Model Details Node Input and Output
The input for a Model Details node is either a Build node (any model type) or a Model node.

- Create Model Details Node
The Model Detail node extracts and provides information about the model and algorithms.

- Edit Model Details Node
The Model Details Node editor enables you to view or specify the models details provided by the node.

- Model Details Automatic Specification
The Automatic Specification setting determines how specifications change automatically.

- Model Details Node Properties
In the Properties pane, you can examine and change the characteristics or properties of a node.

- Model Details Node Context Menu
The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

- Model Details Per Model
The exact data displayed in a Model Details node depends on the particular models.

Related Topics

- About Parallel Processing

- About Oracle Database In-Memory

- Create Table or View Node

Model Details Node Input and Output

The input for a Model Details node is either a Build node (any model type) or a Model node.

All models in Build nodes or Model nodes must have the same mining function type. For example, if one is a Classification model, then all of them must be Classification models.

The output for a Model Details node is a data flow based on the model detail specifications. To enable the data to persist, use a Create Table or View node.

Related Topics

- Create Table or View Node

- Default Model and Output Type Selection

Create Model Details Node

The Model Detail node extracts and provides information about the model and algorithms.

To create a Model Details node, follow these steps:

1. Identify the input node or nodes for model details. The input node must be one or more of the following:

- Any Model Build node

- Any Model node

Note:

All the models selected must have the same mining function type. For example, if one of the nodes is a Classification node, then all other nodes must build Classification models.

2. In the Components pane, go to Workflow Editor. If the Components pane is not visible, then in the SQL Developer menu bar, go to View and click Components. Alternately, press Ctrl+Shift+P to dock the Components pane.

3. In the Workflow Editor expand Models and click Model Details.

4. Drag and drop the node from the Components pane to the Workflow pane.
The node is added to the workflow. The GUI shows that the node has no data associated with it. Therefore, it cannot be run.

5. Move the cursor to the workflow. Right-click one of the input nodes, and select Connect. Drag the link to the Model Details node. Repeat if you must connect more nodes.

6. The default specification for model details depends on the model. To use the default specification, right-click the Model Details node and click Run.

7. To change the specification for the Model Details node, right-click the node and select Edit. Alternately, you can change the specifications in the Properties pane of the node.

Related Topics

- Model Details Automatic Specification

- Edit Model Details Node

Edit Model Details Node

The Model Details Node editor enables you to view or specify the models details provided by the node.

Under the Selected Models section, you can view the models, the nodes, the algorithm and the partition keys. To open Edit Model Details Node, double-click a Model Details node. Alternately, right-click a Model Details node and select Edit.

You can perform the following tasks:

- Auto Setting: If this option is selected (the default), then the system determines the specification. You cannot change the output types, algorithm types, or selected models.

- Function: Displays the function type of the input nodes connected. For example, if a Classification node is connected to Model Details, then the function is Classification. If no input nodes are connected, then it is undefined.

- Model Type: Displays the list of algorithms available, including All. Select a model type.

- Output: Select an output type for the Model Details of the algorithm. The options available are:

- If you select All or O-Clusters in the Model Type field, then available output types are

- Attribute Histogram

- Centroid

- Centroid Scoring (available only for K-Means)

- Full Tree

- Model Signature

- Rules

- If you select Expectation Maximization, then available output types are:

- Attribute Gaussian Distribution

- Attribute Histogram

- Centroid

- Component Bernoulli Distribution

- Component Clusters

- Component Priors

- Components

- Full Tree

- Global Details

- Model Signature

- Projections

- Rules

- If you select R Extensible, then available output types are:

- Model Signature

- R Model Details

- Column: Click Columns to view the list of the columns (name and data type) for the selected output type.

- Add: To add model type or edit output type, deselect Automatic Specification. To add another model type, select the model type and click . The Edit Model Details Node dialog box opens. You can accept the default specifications or edit them.

- Edit Model Selection Details
The Edit Model Selection Details provide generic information related to the mining function, model type, output type, available compatible models and selected models in two sections.

Related Topics

- Edit Model Details Node

- Model Details Automatic Specification

Edit Model Selection Details

The Edit Model Selection Details provide generic information related to the mining function, model type, output type, available compatible models and selected models in two sections.

The top pane of the Edit Model Selection Details dialog box contains general information:

- Function: Displays the function type of the input nodes connected. For example, if a Classification node is connected to Model Details, then the function is Classification. If no input nodes are connected, then it is undefined.

- Model Type: Displays algorithms. If there are models already selected (listed in the Selected Models grid), then the Model Type field is disabled to match the already selected models. If you move all models out of the Selected Models grid, then the Model Type field is enabled again. If the Model Type is enabled, then you can select models. The default is All Models.

- Output Type: Displays the list of possible output types (model queries) that are available for the specified model types. The values for each algorithm selection are as follows:

- Decision Tree (initial default): Full Tree (default), Full Tree XML, Leaf Nodes, Model Signature

- SVM Classification: Coefficients (Default), Model Signature

- SVM Regression, Coefficients (Default), Model Signature

- Naive Bayes: Pair Probabilities (Default), Model Signature

- Association Rules: Rules (Default), Global Details, Itemsets

- Anomaly Detection: Coefficients (Default), Model Signature

- GLM Classification: Statistics (Default), Row Diagnostics, Model Signature, Global Details

- GLM Regression: Statistics (Default), Row Diagnostics, Model Signature, Global Details

- KM or OC Clustering: Full Tree (Default), Rules, Attribute Histograms, Centroid, Model Signature

- Expectation Maximization (EM): Full Tree (Default), Attribute Histograms, Centroid Components, Global Details, Model Signature, Projections, Rules.
EM requires Oracle Database 12c Release 1 (12.1) or later.

- NMF: Features Transactional (Default), Model Signature

- SVD: Features Transactional (Default), Global Details, Model Signature, Projections, Singular Values
SVD requires Oracle Database 12c Release 1 (12.1) or later.

- PCA: Features Transactional (Default), Eigen Values, Global Details, Model Signature, Projections
PCA requires Oracle Database 12c Release 1 (12.1) or later.

Output values are also available for multiple model types. For example, you can select Centroid for all clustering models.

- Columns: Click to see a list of the columns (name and data type) for the selected output type.

The lower section of the dialog box displays the following:

- Available Compatible Models: Lists the available models, that is, models that match the algorithm selection. The grid, for each model, displays the model Name, the input node for the model, and the algorithm used to build the model.

- Selected Models: Lists the selected models. The grid, for each model, displays the model name, the input node for the model, and the algorithm used to build the model.

Model Details Automatic Specification

The Automatic Specification setting determines how specifications change automatically.

- By default, Automatic Specification is set to ON or selected. If Automatic Specification is set to ON, then it results in the following behavior:

- When the first input node is connected to a Model Details node, the input node is searched for models in a default order of priority. For the first model type found, all the nodes matching models are added to the Model Details Specification along with the default Output Type.

- On subsequent connections, the models that match the type in the Model Details node are automatically added. A message is displayed telling you that models are being added automatically.

- When an input node is disconnected, all model specifications provided by that node are automatically removed from the Model Details node.

- When an input node is edited, any models added are automatically added to the Model Details node if the added model matches the model type contained in the node. If models are deleted from an input node, then they are deleted from the Model Details node.

- When a parent node is edited so that all models are removed, the model node is set to undefined. When a new model is added to the parent node, the model node remains undefined because it is too unpredictable about what model and output type would be selected by default given that there may be many parent nodes connected to a model node.

- When an input node is edited and the model is changed so that it is no longer consistent with its specification in the model details node, the model specification is removed.

- If Automatic Specification is Off or deselected, then it results in the following behavior:

- Models are not added automatically.

- You must edit the Model Details node.

- Validations are performed as usual, so models that are now inconsistent or missing are marked as invalid. Also, if models are missing and a node is added that contains a match with that model, then it is made valid and associated to the new node.

- You must manually fix or remove invalid model references.

- Default Model and Output Type Selection
The specification that is automatically added depends on the mining function of the model.

Related Topics

- Edit Model Details Node

Default Model and Output Type Selection

The specification that is automatically added depends on the mining function of the model.

The mining function of the model are as follows:

- Classification

- Decision Tree: Full Tree

- GLM: Statistics

- NB: Probabilities

- SVM: LINEAR KERNEL ONLY Coefficients

- Clustering

- KM: Full Tree

- OC: Full Tree

- EM: Full Tree

- Regression

- GLM: Statistics

- SVM: LINEAR KERNEL ONLY Coefficients

- Anomaly Detection

- SVM: LINEAR KERNEL ONLY Coefficients

- Association

- Apriori: Rules

- Feature Extraction

- NMF, SVD, or PCA: Features transactional

Model Details Node Properties

In the Properties pane, you can examine and change the characteristics or properties of a node.

To view the properties of a node, click the node and click Properties. If the Properties pane is closed, then go to View and click Properties. Alternately, right-click the node and click Go to Properties.

Model Details node Properties has the following sections:

- Models (Model Details)
The Models section lists the models that you want to save details about.

- Output (Model Details)
The Output tab lists the columns produced by the Model Details node.

- Cache (Model Details)
You can generate cache. If you generate cache, then you can specify the sampling size.

- Details
The Details section displays the node name and comments about the node.

Related Topics

- Properties

Models (Model Details)

The Models section lists the models that you want to save details about.

You can add and remove models from the list.

Output (Model Details)

The Output tab lists the columns produced by the Model Details node.

For each column, the alias (if any) and the data type are displayed.

Related Topics

- Model Details Automatic Specification

Cache (Model Details)

You can generate cache. If you generate cache, then you can specify the sampling size.

The default is to not generate cache to optimize the viewing of results. The default sampling size is 2000 rows.

Details

The Details section displays the node name and comments about the node.

You can change the name of the node and edit the comments in this section. The new node name and comments must meet the requirements.

Related Topics

- Node Name and Node Comments

Model Details Node Context Menu

The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

To view the context menu options, right click the node. The following options are available in the context menu:

- Connect

- Edit. Opens the Edit Model Details Node dialog box.

- Validate Parents

- Run

- Force Run

- View Data (Model Details)

- Generate Apply Chain

- Deploy

- Show Graph

- Cut

- Copy

- Paste

- Extended Paste

- Select All

- Performance Settings. This opens the Edit Selected Node Settings dialog box, where you can set Parallel Settings and In-Memory settings for the node.

- Show Event Log

- Show Runtime Errors. Displayed only if there is an error.

- Show Validation Errors. Displayed only if there are validation errors.

- Navigate

- View Data (Model Details)
After a model is built and run successfully, you can view the data contained in the model using the View Data option.

Related Topics

- Edit Model Details Node

View Data (Model Details)

After a model is built and run successfully, you can view the data contained in the model using the View Data option.

To view the complete Model Details output, right-click the node and select View Data.

The output is displayed in the following tabs:

- Data: The data that constitutes the model details. What the data represents depends on the model. For example, the data could represent a tree or rules. You can sort and filter the columns of this tab.

- Columns: Data Type and Mining Type of the columns in the output.

- SQL: SQL used to generate the model details.

Related Topics

- Model Details Per Model

Model Details Per Model

The exact data displayed in a Model Details node depends on the particular models.

All models that can be applied (scored) can have model signature as output.

Related Topics

- Default Model and Output Type Selection

- Edit Model Selection Details

R Build Node

The R Build Node allows you to register R models. It builds R models and generates R model test results for Classification and Regression mining function. R Build nodes supports Classification, Regression, Clustering, and Feature Extraction mining functions only.

You must have Oracle R Enterprise installed in the host to build R models.

Note:
The R Model is visible only when Oracle SQL Developer is connected to Oracle Database 12.2 and later.

- Create R Build Node
Create a R Build Node to register R models.

- Edit R Build Node
The Edit R Build Node dialog box allows you to edit settings related to the R Model.

- Advanced Settings (R Build Node)
The Advanced Settings dialog box allows you to view and edit model settings related to data usage, Extensible settings, and configuration of the previously defined R functions such as build function, scoring function, and model details function.

- R Build Node Properties
In the Properties pane, you can examine and change the characteristics or properties of a node.

- R Build Node Context Menu
The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

Create R Build Node

Create a R Build Node to register R models.

Identify an input node. The input node can be any node that provides data as inputs. Depending on the mining function, the R Build node can also accept a test data source node. This is available only for Classification and Regression mining functions.

To create a R Build node:

1. In the Components pane, go to Workflow Editor. If the Components pane is not visible, then in the SQL Developer menu bar, go to View and click Components. Alternately, press Ctrl+Shift+P to dock the Components pane.

2. In the Workflow Editor, expand Models, and click R Extensible.

3. Drag and drop the node from the Components pane to the Workflow pane.
The node is added to the workflow. The GUI shows that the node has no data associated with it. Therefore, it cannot be run.

4. Right-click the input node and click Connect. Drag the line to the R Build node. You can also create an additional connection from a Test node for Classification and Regression mining function.
This connect the input node to the R Build node.

Edit R Build Node

The Edit R Build Node dialog box allows you to edit settings related to the R Model.

The dialog box comprises the following tabs:

- Build
In the Build tab enables you to specify or change the characteristics of the models to build.

- Partition
In the Partition tab, you can build partitioned models.

- Input
The Input tab specifies the input for model build.

- Sampling
The settings in the Sampling tab are applied to all models in the node. In the Sampling tab, you can specify the row size.

- Text
Text is available for any of the following data types: CHAR, VARCHAR2, BLOB, CLOB, NCHAR, or NVARCHAR2.

Build

In the Build tab enables you to specify or change the characteristics of the models to build.

To edit the characteristics of the model to build, follow these steps:

1. The Function field displays the supported mining functions, which are Classification, Regression, Clustering and Feature Extraction.

2. The Target is enabled only for Classification and Regression models only.

3. In the Case ID field, select a Case ID.

Note:
A case ID is not required. However, if you do not specify a case ID, then the processing will be slower.

4. Additionally, you can perform the following tasks in the Build tab:

- Add Model: To add models, click

- Delete Model: To delete any model, select the model and click

- Edit Model: To edit a model, click

- Duplicate Model: To copy an existing model, select the model and click

5. Click OK.

- Add Model (R Build Node)
You must provide R functions that are compatible with Oracle Data Mining Extensible framework. Otherwise runtime errors may result.

Add Model (R Build Node)

You must provide R functions that are compatible with Oracle Data Mining Extensible framework. Otherwise runtime errors may result.

Note:
The required R functions must be registered using the script rqScriptCreate in Oracle R Enterprise. For more information about the procedure, see Oracle R Enterprise User’s Guide.

To add a model to the R Build node, provide the following details:

1. Name: This is the name of the model.

2. Build Function: Lists all registered R functions. Select the correct R functions to be used for the build process. Click Edit to open the Build Function dialog box.

3. Score Function: Lists all registered R functions. Select the correct R function to be used for scoring. Click Edit to open the Score Function dialog box.
Scoring function is optional. If you do not provide the scoring function, then scoring results will not be available and the nodes that depend on scoring will not recognize the model as valid.

4. Model Details Function: This is an optional function. The Model Details function generates the output in the R Node model viewer in the Details tab. The Model Details node displays the data only if the model details function is provided. Click Edit to open the Model Details Function dialog box.

5. Click OK.

- Build Function
In the Build Function dialog box, you can select any registered R function to be used for the build function.

- Build Settings
The Build Settings dialog box allows you to specify the required settings with names, values, and data types. The names must match the argument names in the R function. The data types can be either NUMBER or STRING.

- Score Function
In the Score Function dialog box, you can select a registered R function to be used for scoring.

- Model Details Function
In the Model Details Function dialog box, you can select a registered R function.

Related Topics

- Oracle R Enterprise User’s Guide

Build Function

In the Build Function dialog box, you can select any registered R function to be used for the build function.

1. The Build Function field displays the applicable R build function. You can select another function from the drop-down list.

2. The Function Definition field displays the code of the selected function. You can verify the function here. You can specify algorithm settings to be passed on to the build function.

3. Click Settings. This opens the Build Settings dialog box where you can specify values for parameters used in the build function.

4. Click OK.

Related Topics

- Build Text

Build Settings

The Build Settings dialog box allows you to specify the required settings with names, values, and data types. The names must match the argument names in the R function. The data types can be either NUMBER or STRING.

1. Select Specify Row Weight Column and select an option from the drop-down list. The option is enabled for Generalized Linear Models (GLM), that includes Classification and Regression models only.

2. In the Settings section:

- Click to add a setting.

- Select a setting and click to delete the selected setting.

3. Click OK.

Score Function

In the Score Function dialog box, you can select a registered R function to be used for scoring.

1. The Score Function field displays the applicable R build function. You can select another function from the drop-down list.

Note:
If the scoring function is not specified, then the R model will not be available for Test and Apply operations.

2. The Function Definition field displays the code of the selected function. You can verify the function here.

3. In the Weight Function field, select an applicable R Weight function from the drop-down list. This is required for Prediction Details.

4. The Function Definition field displays the details of the selected R weight function.

5. Click OK.

Model Details Function

In the Model Details Function dialog box, you can select a registered R function.

1. In the Model Details Function field, select the R function as applicable. If you do not specify the Model Details function, then the Details tab in the Model Viewer will not be available.

2. The Function Definition section displays the code of the selected R function. You can verify the function here. The selected model detail function generates a data frame that is persisted to a view, after the model is built.

3. In the Output Column section, you must specify the output signature of the function. The output signature of the function should match the data frame object generated by the function. For example, if you select a R function that produces an output two columns ATTRIBUTE and COEFFICIENTS, then the column data types can be either NUMBER or VARCHAR2. Internally, Oracle Data Miner will construct a SELECT statement from the specified name-value pairs to be passed to the R Model Details function using the ODM extensible framework.

4. Click OK.

Partition

In the Partition tab, you can build partitioned models.

- In the Maximum Number of Partitions field, set a value by clicking the arrows. This sets the partition cutoff value. The cutoff value must be greater than zero. If this option is not selected, then the native Oracle Data Miner cutoff value is used, which can be a very large value.

- Click Advanced Settings to set and select the type of partition build.

- To add columns for partitioning, click .

Note:

Only NUMBER and VARCHAR2 columns can be selected as partition columns. Case ID and Target columns cannot be selected as partition columns.

- To remove a partitioning column, select the columns and click .

- To move a column to the top, click .

- To move a column up, click

- To move a column down, click

- To move a column to the bottom, click

- Add Partitioning Columns
Partitioning columns result in building a virtual model for each unique partition. Because the virtual model uses data only from a specific partition, it can potentially predict cases more accurately than if you did not select a partition.

Add Partitioning Columns

Partitioning columns result in building a virtual model for each unique partition. Because the virtual model uses data only from a specific partition, it can potentially predict cases more accurately than if you did not select a partition.

In addition to selecting attributes, you can specify partitioning expressions. Partitioning expressions are concatenated and the result expression is the same for all predictive functions.

1. Select one or more attributes in the Available Attributes list to serve as partitions.

2. Move the selected columns to the Selected Attributes list using the arrows.

3. Click OK. The attributes are moved to the Partition list.

Optionally, you can add partitioning expressions.

Input

The Input tab specifies the input for model build.

Determine inputs automatically (using heuristics) is selected by default for all models. Oracle Data Miner decides which attributes to use for input. For example, attributes that are almost constant may not be suitable for input. Oracle Data Miner also determines mining type and specifies that auto data preparation is performed for all attributes.

Note:
For R Build nodes, Auto Data Preparation is not performed.

After the node runs, rules are displayed explaining the heuristics. Click Show for detailed information.

You can change these selections. To do so, deselect Determine inputs automatically (using heuristics).

Related Topics

- Data Used for Model Building

Sampling

The settings in the Sampling tab are applied to all models in the node. In the Sampling tab, you can specify the row size.

By default, Sampling is set to OFF. To set it to ON:

1. Click ON, and then select:

- System Determined

- User Specified and specify the row size

2. Click OK.

Text

Text is available for any of the following data types: CHAR, VARCHAR2, BLOB, CLOB, NCHAR, or NVARCHAR2.

If you are connected to Oracle Database 12c Release 1 (12.1) and later, then you can specify text characteristics in the Text tab in the Edit Model Build dialog box.

If you specify text characteristics in the Text tab, then you are not required to use the Text nodes.

Note:

If you are connected to Oracle Database 11g Release 2 (11.2) or earlier, then use Text nodes. The Text tab is not available in Oracle Database 11g Release 2 and earlier.

To examine or specify text characteristics for data mining, either double-click the build node or right-click the node and select Edit from the context menu. Click the Text tab.

The Text tab enables you to modify the following:

- Categorical cutoff value: Enables you to control the cutoff used to determine whether a column should be considered as a Text or Categorical mining type. The cutoff value is an integer. It must be 10 or greater and less than or equal to 4000. The default value is 200.

- Default Transform Type: Specifies the default transformation type for column-level text settings. The values are:

- Token (Default): For Token as the transform type, the Default Settings are:

- Languages: Specifies the languages used in the documents. The default is English. To change this value, select an option from the drop-down list. You can select more than one language.

- Bigram: Select this option to mix the NORMAL token type with their bigram. For example, New York. The token type is BIGRAM.

- Stemming: By default, this option is not selected. Not all languages support stemming. If the language selected is English, Dutch, French, German, Italian, or Spanish, then stemming is automatically enabled. If Stemming is enabled, then stemmed words are returned for supported languages. Otherwise the original words are returned.

Note:

If both Bigram and Stemming are selected, then the token type is STEM_BIGRAM. If neither Bigram nor Stemming is selected, then token type is NORMAL.

- Stoplists: Specifies the stoplist to use. The default setting is to use the default stoplist. You can add stoplists or edit stoplists. If you select more than one language, and the selected stoplist is Default, then the default stop words for languages are added to the default stoplist from the repository. No duplicate stop words are added.

- Click to view the Stoplist Details. This opens the Stoplist Details dialog box.

- Click to add a new stoplist. This opens the New Stoplist Wizard.

- Tokens: Specify the following:

- Max number of tokens across all rows (document). The default is 3000.

- Min number of rows (document) required for a token

- Theme: If Theme is selected, then the Default Settings are as follows:

- Language: Specifies the languages used in the documents. The default is English. To change this value, select one from the drop-down list. You can select more than one language.

- Stoplists: Specifies the stoplist to use. The default setting is to use the default stoplist. You can add stoplists or edit stoplists. If you select more than one language and the selected stoplist is Default, then the default stop words for languages are added to the default stoplist (from the repository). No duplicate stop words are added.

- Themes: Specifies the maximum number of themes across all documents. The default is 3000.

- Synonym: The Synonym tab is enabled only if a thesaurus is loaded. By default, no thesaurus is loaded. You must manually load the default thesaurus provided by Oracle Text or upload your own thesaurus.

- Click Stoplists to open the Stoplist Editor. You can view, edit, and create stoplists.
You can use the same stoplist for all text columns.

Related Topics

- Oracle Text Concepts

- Text Nodes

- Stoplist Editor

Advanced Settings (R Build Node)

The Advanced Settings dialog box allows you to view and edit model settings related to data usage, Extensible settings, and configuration of the previously defined R functions such as build function, scoring function, and model details function.

You can perform the following tasks:

- Add Model: Click to add a model.

- Delete Model: Select a model and click .

R Build Node Properties

In the Properties pane, you can examine and change the characteristics or properties of a node.

To view the properties of a node, click the node and click Properties. If the Properties pane is closed, then go to View and click Properties. Alternately, right-click the node and click Go to Properties.

The R Build node Properties pane has the following sections:

- Models

- Build

- Partition

- Test

- Details

R Build Node Context Menu

The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

The following options are available in the context menu:

- Connect

- Run

- Force Run

- Create Schedule

- Edit. This opens the Edit R Build Node dialog box.

- Advanced Settings

- View Models

- View Test Results

- Compare Test Results

- Generate Apply Chain

- Show Event Log

- Show Validation Errors

- Validate Parents

- Deploy

- Cut

- Copy

- Paste

- Extended Paste

- Select All

- Performance Settings. This opens the Edit Selected Node Settings dialog box, where you can set Parallel Settings and In-Memory settings for the node.

- Go To Properties

- Navigate

Regression Node

The Regression node defines one or more Regression models to build and to test.

To specify data for the build, connect a Data Source node to the Regression node. You can also connect a second data source to the Regression build node to specify test data. You can only specify one target. A Regression build can run in parallel.

The models in a Regression Node all have the same target and case ID.

There are two ways to make regression predictions:

- By building and testing a Regression model: Use a Regression node, and then apply the model to new data to make classifications.

- By using a Prediction Query, which is one of the predictive queries.

This section consists of the following topics:

- Default Behavior for Regression Node
For a binary target, the Regression node builds four models.

- Create a Regression Node
By default, a Regression node builds two models, one each using General Linear Model (GLM) and Support Vector Machine (SVM) algorithm.

- Data for Model Build
Oracle Data Miner uses heuristic techniques on data for model build.

- Edit Regression Build Node
In the Edit Regression Build Node dialog box, you can edit settings related to the model build, model partition, sampling, inputs, text settings and so on.

- Advanced Settings for Regression Models
In the Advanced Settings dialog box, you can add models, delete models, review settings, and change settings related to the model and algorithm.

- Regression Node Properties
In the Properties pane, you can examine and change the characteristics or properties of a node.

- Regression Node Context Menu
The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

Related Topics

- Prediction Query

- About Parallel Processing

Default Behavior for Regression Node

For a binary target, the Regression node builds four models.

The models are built using the following algorithms:

- Generalized Linear Model (GLM)

- Support Vector Machine (SVM)

The models have the same build data and the same target.

By default, the models are all tested. The test data is created by randomly splitting the build data into a build data set and a test data set. The default ratio for the split is 60 percent build and 40 percent test. When possible Data Miner uses compression when creating the test and build data sets.

You can instead use all the build data as test data.

To use separate test data, connect a test data source to the Build node or use a Test node.

After you test models, you can view test results.

You can compare test results for two or more Regression models using the Compare Test Results selection of the context menu.

The case ID is optional. However, if you do not specify a case ID, then the processing will be slower.

Related Topics

- Create Table Node and Compression

- Regression Model Test Viewer

- Test Node

Create a Regression Node

By default, a Regression node builds two models, one each using General Linear Model (GLM) and Support Vector Machine (SVM) algorithm.

Before creating a Regression node, first, create a workflow. Then, identify or create a data source.

To create a Regression node and attach data to it:

1. In the Components pane, go to Workflow Editor. If the Components pane is not visible, then in the SQL Developer menu bar, go to View and click Components. Alternately, press Ctrl+Shift+P to dock the Components pane.

2. In the Workflow Editor. expand Models, and click Regression.

3. Drag and drop the node from the Components pane to the Workflow pane.
The node is added to the workflow. The GUI shows that the node has no data associated with it. Therefore, it cannot be run.

4. Move to the node that provides data for the build. Right-click, and click Connect. Drag the line to the Regression node and click again.

5. The Edit Regression Build Node dialog box opens. You must specify a target (all models in the node have the same target). A target cannot be text.

6. To specify a separate Data Source node for test, connect a second Data Source node to the Build node. This is optional.

7. After you finish editing the node, and connecting the optional test Data Source node, the node should be ready to build. Right-click the node and click Run.

If you specified a test Data Source node when the node runs, then the connection from the build data source is labeled Build and the connection from the test data source is labeled Test.

Related Topics

- Edit Regression Build Node

Data for Model Build

Oracle Data Miner uses heuristic techniques on data for model build.

Oracle Data Miner uses heuristics to:

- Determine the attributes of the input data used for model build.

- Determine the mining type of each attribute.

Related Topics

- Data Used for Model Building

Edit Regression Build Node

In the Edit Regression Build Node dialog box, you can edit settings related to the model build, model partition, sampling, inputs, text settings and so on.

To open the Edit Regression Build Node dialog box, double-click a Regression Build node, or right-click a Regression Build node and select Edit.

The Edit Regression Build Node dialog box contains the following tabs:

- Build
The Build tab enables you to specify or change the characteristics of the models to build.

- Partition
In the Partition tab, you can build partitioned models.

- Sampling
The settings in the Sampling tab are applied to all models in the node. In the Sampling tab, you can specify the row size.

- Input
The Input tab specifies the input for model build.

- Text
Text is available for any of the following data types: CHAR, VARCHAR2, BLOB, CLOB, NCHAR, or NVARCHAR2.

Related Topics

- Viewing and Changing Data Usage

Build

The Build tab enables you to specify or change the characteristics of the models to build.

To edit the characteristics of the model to build, follow these steps:

1. In the Target field, select a target from the drop-down list. The list consist of attributes from the table or view specified in the Data Source node that is connected to the build node.

You must specify a target. All models in the node have the same target.

2. In the Case ID field, select one attribute from the drop-down list. This attribute must uniquely identify a case.

Note:

A case ID is not required. However, if you do not specify a case ID, then the processing will be slower.

A case ID is required to generate GLM diagnostics.

If you specify a case ID, then all models in the node have the same case ID.

3. In the Model Settings list, select which models you want to build. You can build Support Vector Machine (SVM) and Generalized Linear Models (GLM). You can delete any of these models by selecting the model and clicking

- To delete any model, select the model and click .

- To add models, click .

- To edit a model, click .

- To copy an existing model, select the model and click

4. Click OK.

The default is to test the model using a test data set created by splitting the build data set. If you do not want to test the model in this way, then go to the Test section in the Regression node Properties pane. You can also use a Test Node and a test Data Source node to test the model instead.

- Add Model (Regression)
In the Add Model dialog box, you can add a model to the node, and select an algorithm for it.

Related Topics

- Advanced Settings Overview

- No Case ID

- Test (Regression)

Add Model (Regression)

In the Add Model dialog box, you can add a model to the node, and select an algorithm for it.

To add a model to the node:

1. In the Algorithm field, select an algorithm.

2. In the Name field, a default name is displayed. You can use the default or rename the model.

3. In the Comment field, add comments if any. This is an optional field.

4. Click OK. The new model is added to the node.

Partition

In the Partition tab, you can build partitioned models.

- In the Maximum Number of Partitions field, set a value by clicking the arrows. This sets the partition cutoff value. The cutoff value must be greater than zero. If this option is not selected, then the native Oracle Data Miner cutoff value is used, which can be a very large value.

- Click Advanced Settings to set and select the type of partition build.

- To add columns for partitioning, click .

Note:

Only NUMBER and VARCHAR2 columns can be selected as partition columns. Case ID and Target columns cannot be selected as partition columns.

- To remove a partitioning column, select the columns and click .

- To move a column to the top, click .

- To move a column up, click

- To move a column down, click

- To move a column to the bottom, click

Sampling

The settings in the Sampling tab are applied to all models in the node. In the Sampling tab, you can specify the row size.

By default, Sampling is set to OFF. To set it to ON:

1. Click ON, and then select:

- System Determined

- User Specified and specify the row size

2. Click OK.

Input

The Input tab specifies the input for model build.

Determine inputs automatically (using heuristics) is selected by default for all models. Oracle Data Miner decides which attributes to use for input. For example, attributes that are almost constant may not be suitable for input. Oracle Data Miner also determines mining type and specifies that auto data preparation is performed for all attributes.

Note:
For R Build nodes, Auto Data Preparation is not performed.

After the node runs, rules are displayed explaining the heuristics. Click Show for detailed information.

You can change these selections. To do so, deselect Determine inputs automatically (using heuristics).

Related Topics

- Data Used for Model Building

Text

Text is available for any of the following data types: CHAR, VARCHAR2, BLOB, CLOB, NCHAR, or NVARCHAR2.

If you are connected to Oracle Database 12c Release 1 (12.1) and later, then you can specify text characteristics in the Text tab in the Edit Model Build dialog box.

If you specify text characteristics in the Text tab, then you are not required to use the Text nodes.

Note:

If you are connected to Oracle Database 11g Release 2 (11.2) or earlier, then use Text nodes. The Text tab is not available in Oracle Database 11g Release 2 and earlier.

To examine or specify text characteristics for data mining, either double-click the build node or right-click the node and select Edit from the context menu. Click the Text tab.

The Text tab enables you to modify the following:

- Categorical cutoff value: Enables you to control the cutoff used to determine whether a column should be considered as a Text or Categorical mining type. The cutoff value is an integer. It must be 10 or greater and less than or equal to 4000. The default value is 200.

- Default Transform Type: Specifies the default transformation type for column-level text settings. The values are:

- Token (Default): For Token as the transform type, the Default Settings are:

- Languages: Specifies the languages used in the documents. The default is English. To change this value, select an option from the drop-down list. You can select more than one language.

- Bigram: Select this option to mix the NORMAL token type with their bigram. For example, New York. The token type is BIGRAM.

- Stemming: By default, this option is not selected. Not all languages support stemming. If the language selected is English, Dutch, French, German, Italian, or Spanish, then stemming is automatically enabled. If Stemming is enabled, then stemmed words are returned for supported languages. Otherwise the original words are returned.

Note:

If both Bigram and Stemming are selected, then the token type is STEM_BIGRAM. If neither Bigram nor Stemming is selected, then token type is NORMAL.

- Stoplists: Specifies the stoplist to use. The default setting is to use the default stoplist. You can add stoplists or edit stoplists. If you select more than one language, and the selected stoplist is Default, then the default stop words for languages are added to the default stoplist from the repository. No duplicate stop words are added.

- Click to view the Stoplist Details. This opens the Stoplist Details dialog box.

- Click to add a new stoplist. This opens the New Stoplist Wizard.

- Tokens: Specify the following:

- Max number of tokens across all rows (document). The default is 3000.

- Min number of rows (document) required for a token

- Theme: If Theme is selected, then the Default Settings are as follows:

- Language: Specifies the languages used in the documents. The default is English. To change this value, select one from the drop-down list. You can select more than one language.

- Stoplists: Specifies the stoplist to use. The default setting is to use the default stoplist. You can add stoplists or edit stoplists. If you select more than one language and the selected stoplist is Default, then the default stop words for languages are added to the default stoplist (from the repository). No duplicate stop words are added.

- Themes: Specifies the maximum number of themes across all documents. The default is 3000.

- Synonym: The Synonym tab is enabled only if a thesaurus is loaded. By default, no thesaurus is loaded. You must manually load the default thesaurus provided by Oracle Text or upload your own thesaurus.

- Click Stoplists to open the Stoplist Editor. You can view, edit, and create stoplists.
You can use the same stoplist for all text columns.

Related Topics

- Oracle Text Concepts

- Text Nodes

- Stoplist Editor

Advanced Settings for Regression Models

In the Advanced Settings dialog box, you can add models, delete models, review settings, and change settings related to the model and algorithm.

The Advanced Settings dialog box enables you to:

- Inspect and change data usage and algorithm settings for each model in the node

- Add and delete models

To change or view Advanced Settings, click in the Edit Regression Build Node dialog box. Alternately, right-click the node and select Advanced Settings.

The upper panes lists all the models in the node. You can perform the following functions:

- Delete: To delete a model, select the model and click

- Add: To add a model, click . The Add Model dialog box opens.

In the lower pane, you can view and modify data usage and algorithm settings for the model selected in the upper pane. You can edit the following:

- Data Usage

- Algorithm Settings
The settings that can be changed depend on the algorithm:

- GLM Regression Algorithm Settings

- SVM Regression Algorithm Settings

Related Topics

- Edit Regression Build Node

- Add Model (Regression)

Regression Node Properties

In the Properties pane, you can examine and change the characteristics or properties of a node.

To view the properties of a node, click the node and click Properties. If the Properties pane is closed, then go to View and click Properties. Alternately, right-click the node and click Go to Properties.

Before building Regression models, ensure the following:

- Specify a Target.

- Specify a case ID. This is optional. However, if you do not specify a case ID, then the processing will be slower.

This section contains the following topics:

- Models (Regression)
The Model section lists the models that are built.

- Build (Regression)
The Build section displays information related to the selected target and the Case ID.

- Partition
In the Partition tab, you can build partitioned models.

- Details
The Details section displays the node name and comments about the node.

- Test (Regression)
The Test section specifies the data used for testing and the tests performed.

Models (Regression)

The Model section lists the models that are built.

By default, three Regression models are built using three different algorithms (SVM, NB, and DT). You can also specify the GLM algorithm if you add a model.

You can perform the following tasks:

- Delete: To delete a model, select the model and click .

- Add: To add a model, click

- Compare Test Results: If models were tested, then you can compare test results. Select two or more models and click

- View Models: If a model built successfully, then you can view the model. Select the model and click . The corresponding viewer opens.

- Indicate Model Status: Indicates whether models are passed to subsequent nodes.

- Output Column
The Output column in the Model Settings grid controls the passing of models to subsequent nodes. By default, all models are passed to subsequent nodes.

Related Topics

- Support Vector Machine

- Naive Bayes

- Decision Tree

- Generalized Linear Models

Output Column

The Output column in the Model Settings grid controls the passing of models to subsequent nodes. By default, all models are passed to subsequent nodes.

- To ignore a model, that is, to not pass it to subsequent nodes, click . The icon changes to , the Ignore icon.

- To cancel the ignore, click the Ignore icon again. It changes to the Output icon.

Build (Regression)

The Build section displays information related to the selected target and the Case ID.

The Build section displays the following:

- Target: The Build node must be connected to a Data Source node. You then select the target from the target list. To change the target, select a different target from the drop-down list.

- Case ID: Select an attribute from the drop-down list. This attribute must uniquely identify a case. The case ID is optional. If no case ID is selected, then None is displayed. However, if no case ID is specified, then the processing will be slower.

Partition

In the Partition tab, you can build partitioned models.

- In the Maximum Number of Partitions field, set a value by clicking the arrows. This sets the partition cutoff value. The cutoff value must be greater than zero. If this option is not selected, then the native Oracle Data Miner cutoff value is used, which can be a very large value.

- Click Advanced Settings to set and select the type of partition build.

- To add columns for partitioning, click .

Note:

Only NUMBER and VARCHAR2 columns can be selected as partition columns. Case ID and Target columns cannot be selected as partition columns.

- To remove a partitioning column, select the columns and click .

- To move a column to the top, click .

- To move a column up, click

- To move a column down, click

- To move a column to the bottom, click

Details

The Details section displays the node name and comments about the node.

You can change the name of the node and edit the comments in this section. The new node name and comments must meet the requirements.

Related Topics

- Node Name and Node Comments

Test (Regression)

The Test section specifies the data used for testing and the tests performed.

By default, all models that are built using test data are tested. The test data is created randomly splitting the build data.

The following settings are available in the Test section:

- Perform Test: By default, all models that are built using test data are tested. The test data is created randomly splitting the build data. The default test results are:

- Performance Metrics

- Residuals
You can deselect both.

- Test Data: Test Data is created is one of the following ways:

- Use all of the Mining Build Data for Testing

- Use Split Build Data for TestingSplit for Test (%) Create Split as: View (default). The split creates a view that is not parallel.

- Use a Test Data Source for Testing: Select this option to provide a separate test Data Source and connect the test data source to the build node after you connect the build data. Alternately, you can test a model by using a Test node.

Related Topics

- Testing Regression Models

- Test Node

Regression Node Context Menu

The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

To view the context menu options, right click the node. The following options are available in the context menu:

- Connect

- Edit: Opens the Edit Regression Build Node dialog box.

- Advanced Settings: Opens the Advanced Settings for Regression Models dialog box.

- Validate Parents

- Run

- Force Run

- View Models

- View Test Results

- Compare Test Results

- Generate Apply Chain

- Cut

- Copy

- Paste

- Extended Paste

- Select All

- Performance Settings. This opens the Edit Selected Node Settings dialog box, where you can set Parallel Settings and In-Memory settings for the node.

- Show Event Log

- Show Runtime Errors, displayed only if there is an error

- Show Validation Errors, displayed only if there are validation errors

- Navigate

Related Topics

- Advanced Settings for Regression Models

- Edit Regression Build Node

Advanced Settings Overview

The Advanced Settings dialog box enables you to edit data usage and other model specifications, add and remove models from the node.

You can open the Advanced Settings dialog box in one of these ways:

- Right-click any model node and click Advanced Settings from the context menu.

- Double-click the node to open the editor. Then click .

The dialog box has two panes, as illustrated in this example of Advanced Settings for a Classification Build node:

In the upper pane of the Advanced Settings, you can delete and add models. You can also select models in the upper pane to change data usage. In the lower pane of the Advanced Settings, which has one, two, or three tabs, you can edit model specifications.

- Upper Pane of Advanced Settings
The upper pane of Advanced Settings lists all of the models in the node.

- Lower Pane of Advanced Settings
The lower pane of Advanced Settings displays information related to data usage, algorithm settings, and performance settings.

Upper Pane of Advanced Settings

The upper pane of Advanced Settings lists all of the models in the node.

The Model Settings grid provides the following information about each model:

- Model Name

- Algorithm

- Date of Last Build

- Auto

- Data Usage

- Column Excluded By...

To view the input and mining type for attributes, select the model in the upper pane and deselect Auto. If Auto is selected (the default), then the system automatically determines the attributes used to build the model.

Oracle Data Miner does not necessarily select all attributes to use in building a model. For example, if most of values of an attribute are the same, then the attribute is not selected.

To see which attributes are selected, deselect Auto. Select a model. The lower pane indicates the selected attributes with a check mark in the Input column.

If Auto is not selected, then you can override the system's choices in the Data Usage tab. If Auto is not selected, then you can also view input and mining type. This enables you to see which attributes are used for model build, and to change them, if necessary.

The Model Settings grid enables you to delete or add models to the node.

- Delete: To delete a model, select the model and click .

- Add: To add a model to the node, click . The Add Models dialog box for the node opens. In the Add Model dialog box, select an algorithm, either accept the default name or specify a different name, and add optional comments.

Related Topics

- Viewing and Changing Data Usage

- Data Usage

Lower Pane of Advanced Settings

The lower pane of Advanced Settings displays information related to data usage, algorithm settings, and performance settings.

Select a model in the upper pane. The related information is displayed in the following tabs:

- Data Usage: For all models except Association

- Algorithm Settings: For all models

- Performance Settings: For Classification models only

These tabs display the specification used to build the selected model. You can change the specification.

- Data Usage
The Data Usage tab contains the data grid that lists all attributes in the data source.

- Algorithm Settings
The Algorithm Settings section displays the values of algorithm settings.

- Performance Settings
The performance settings are available for Classification models only.

Data Usage

The Data Usage tab contains the data grid that lists all attributes in the data source.

The Data Usage tab is not supported for the Association node. To modify any values, to see which attributes are not used as input, or to see mining types, select View in the lower pane.

You can change data usage information for several models at the same time. For each attribute, the grid lists displays the following:

- Attributes: This is the name of the attribute.

- Data Type: This is the Oracle Database data type of the attribute.

- Input: Indicates if the attribute is used to build the model. To change the input type, click Automatic. Then click the icon and select the new icon. For models that have a target, such as Classification and Regression models, the target is marked with a red target icon.

- The icon indicates that the attribute is used to build the model.

- The icon indicates that the attribute is ignored, that is, it is not used to build the model.

- Mining Type: This is the logical type of the attribute, either Numerical (numeric data), Categorical (character data), nested numerical, or nested categorical, text or custom text. If the attribute has a type that is not supported for mining, then the column is blank. Mining type is indicated by an icon. Move the cursor over the icon to see what the icon represents. To change the mining type, click Automatic and then click the type for the attribute. Select a new type from the list. You can change mining types as follows:

- Numerical can be changed to Categorical. Changing to Categorical casts the numerical value to string.

- Categorical.

- Nested Categorical and Nested Numerical cannot be changed.

- Auto Prep: If Auto Prep is selected, then automatic data preparation is performed on the attribute. If Auto Prep is not selected, then no automatic data preparation is performed for the attribute. In this case, you are required to perform any data preparation, such as normalization, that may be required by the algorithm used to build the model. No data preparation is done (or required) for target attributes. The default is to perform automatic data preparation.

- Rules: After a model runs, Rules describe the heuristics used. For details, click Show.

There are two types of reasons for not selecting an attribute as input:

- The attribute has a data type that is not supported by the algorithm used for model build.
For example, O-Cluster does not support nested data types such as DM_NESTED_NUMERICALS. If you use an attribute with type DM_NESTED_NUMERICALS to build a O-Cluster model, then the build fails.

- The attribute does not provide data useful for mining. For example, an attribute that has constant or nearly constant values.
If you include attributes of this kind, then the model has lower quality than if you exclude them.

Related Topics

- Viewing and Changing Data Usage

- Automatic Data Preparation (ADP)

Algorithm Settings

The Algorithm Settings section displays the values of algorithm settings.

The settings are determined by the algorithm used to build the model.

Performance Settings

The performance settings are available for Classification models only.

The Performance Settings tab defines the performance objective for Classification model build. To view or change performance settings for a model, select the model in the upper pane. Weights are listed in the Weights grid. Select one of these settings:

- Balanced: (default) Attempts to achieve the best overall accuracy across all the target class values. This is done in different ways depending on the algorithm selected. Generally, it requires the model build process to be biased using weight values that provide extra weight to target values that occur less frequently.

- Natural: Enables the model to build without any bias, so that the model uses its natural view of the data to build an accurate model. In this case, rare target class values are probably not going to be predicted as frequently as they would predict the model that was built using the balanced option.

- Custom: Enables you to enter a set of weights for each target value. One way to get started defining custom weights is to click Balanced or Natural , just above the Weights grid. Either of these options generate weights similar to those that would result in either Balanced or Natural performance. You can then change these weights to different values.

To save the values, click OK.

Related Topics

- Lift Detail

Mining Functions

Mining functions represent a class of mining problems that can be solved using data mining algorithms.

When creating a data mining model, you must first specify the mining function and then choose an appropriate algorithm to implement the function if one is not provided by default.

Oracle Data Mining supports these mining functions:

- Classification
Classification is a data mining function that assigns items in a collection to target categories or classes, that is, items are classified according to target categories.

- Regression
Regression is a data mining function that predicts a number. Profit, sales, mortgage rates, house values, square footage, temperature, or distance could all be predicted using regression techniques.

- Anomaly Detection
Anomaly Detection (AD) identifies cases that are unusual within data that is apparently homogeneous.

- Clustering
Clustering finds natural groupings of data objects, that is objects that are similar in some sense to one another.

- Association
Association rules express the relationships between items that take place at the same time.

- Feature Extraction and Selection
The Feature Extraction mining function combines attributes into a new reduced set of features. The Feature Selection mining function selects the most relevant attributes.

Classification

Classification is a data mining function that assigns items in a collection to target categories or classes, that is, items are classified according to target categories.

fication The goal of classification is to accurately predict the target class for each case in the data. For example, a Classification model could be used to identify loan applicants as low, medium, or high credit risks.

The target categories for a classification are discrete and not ordered. The simplest type of classification problem is binary classification. In binary classification, the target attribute has only two possible values: for example, high credit rating or low credit rating. Multiclass targets have more than two values: for example, low, medium, high, or unknown credit rating.

The following topics describe the classification:

- Building Classification Models
A Classification model is built from historical data for which the classifications are known.

- Comparing Classification Models
You can compare Classification models by comparing the test metrics of the respective models.

- Applying Classification Models
Scoring or applying a Classification model results in class assignments and the probability that the assignment is the correct one.

- Classification Algorithms
Decision Tree algorithm, Naive Bayes algorithm, and Generalized Linear Model algorithms are used for classification.

Building Classification Models

A Classification model is built from historical data for which the classifications are known.

To build (train) a Classification model, a classification algorithm finds relationships between the values of the predictors and the values of the target. Different classification algorithms use different techniques for finding relationships. These relationships are summarized in a model. The model can then be applied to a different data set in which the class assignments are unknown.

Algorithm settings control model build. Settings depend on the algorithm.

Use a Build Node to build one or more Classification models.

Classification models are tested by default.

Related Topics

- Testing Classification Models

- Tuning Classification Models

Comparing Classification Models

You can compare Classification models by comparing the test metrics of the respective models.

Related Topics

- Test Metrics for Classification Models

- Compare Classification Test Results

Applying Classification Models

Scoring or applying a Classification model results in class assignments and the probability that the assignment is the correct one.

For example, a model that classifies customers as low, medium, or high value would also predict the probability that the classification is correct.

Use an Apply node to score a Classification model, that is to apply the model to new data.

Related Topics

- Apply Node

Classification Algorithms

Decision Tree algorithm, Naive Bayes algorithm, and Generalized Linear Model algorithms are used for classification.

- Decision Tree algorithm automatically generates rules, which are conditional statements that reveal the logic used to build the tree.

- Naive Bayes algorithm uses Bayes' Theorem, a formula that calculates a probability by counting the frequency of values and combinations of values in the historical data.

- Generalized Linear Models (GLM) algorithm is a popular statistical technique for linear modeling. Oracle Data Mining implements GLM for binary classification and for regression.
GLM provides extensive coefficient statistics and model statistics, and row diagnostics. GLM also supports confidence bounds, which are the upper and lower boundaries of an interval in which the predicted value is likely to lie.

- Support Vector Machine (SVM) algorithm is a powerful, state-of-the-art algorithm based on linear and non-linear regression. Oracle Data Mining implements SVM for binary and multiclass classification.
Oracle Data Mining implements SVM for binary and multiclass classification.

Related Topics

- Decision Tree

- Naive Bayes

- Generalized Linear Models

- Support Vector Machine

Regression

Regression is a data mining function that predicts a number. Profit, sales, mortgage rates, house values, square footage, temperature, or distance could all be predicted using regression techniques.

For example, a Regression model could be used to predict the value of a house based on location, number of rooms, lot size, and other factors.

This section on Regression contains the following topics:

Regression models are tested by default.

- Building Regression Models
Use a Build Node to build one or more Regression models.

- Applying Regression Models
Scoring, or applying, a Regression model results in class assignments and the probability that the assignment is correct for each case.

- Regression Algorithms
Oracle Data Mining supports Generalized Linear Models (GLM) and Support Vector Machines (SVM) for Regression.

Related Topics

- Testing Regression Models

Building Regression Models

Use a Build Node to build one or more Regression models.

Algorithm settings control the model build. Settings depend on the algorithm.

A Regression task begins with a data set in which the target values are known. For example, a Regression model that predicts house values could be developed based on observed data for many houses over a period of time. In addition to the value, the data might track the age of the house, square footage, number of rooms, taxes, school district, proximity to shopping centers, and so on. House value would be the target, the other attributes would be the predictors, and the data for each house would constitute a case.

In the model build (training) process, a regression algorithm estimates the value of the target as a function of the predictors for each case in the build data. These relationships between predictors and target are summarized in a model, which can then be applied to a different data set in which the target values are unknown.

Applying Regression Models

Scoring, or applying, a Regression model results in class assignments and the probability that the assignment is correct for each case.

For example, a model that predicts a value for each cased also predicts the probability that the value is correct.

Use an Apply node to score a Regression model, that is to apply the model to new data.

Related Topics

- Apply Node

Regression Algorithms

Oracle Data Mining supports Generalized Linear Models (GLM) and Support Vector Machines (SVM) for Regression.

- Generalized Linear Models (GLM) algorithm is a popular statistical technique for linear modeling. Oracle Data Mining implements GLM for binary classification and for regression.
GLM provides extensive coefficient statistics and model statistics, and row diagnostics. GLM also supports confidence bounds.

- Support Vector Machines (SVM) algorithm is a powerful, state-of-the-art algorithm based on linear and non-linear regression.
SVM regression supports two kernels: the Gaussian Kernel for non-linear regression, and the Linear Kernel for linear regression. SVM also supports active learning.

Related Topics

- Generalized Linear Models

- Support Vector Machine

Anomaly Detection

Anomaly Detection (AD) identifies cases that are unusual within data that is apparently homogeneous.

Standard classification algorithms require the presence of both positive and negative examples (counterexamples) for a target class. One-Class Support Vector Machine (SVM) classification requires only the presence of examples of a single target class.

The model learns to discriminate between the known examples of the positive class and the unknown negative set of counter examples. The goal is to estimate a function that is:

- Positive, if an example belongs to a set, and

- Negative or zero, if the example belongs to the complement of the set

Note:

Solving a one-class classification problem can be difficult. The accuracy of one-class classifiers cannot usually match the accuracy of standard classifiers built with meaningful counterexamples.

This section about Anomaly Detection models contains the following topics:

- Building Anomaly Detection Models
Oracle Data Mining uses SVM as the one-class classifier for Anomaly Detection (AD).

- Applying Anomaly Detection Models
Oracle Data Mining uses Support Vector Machine (SVM) as the one-class classifier for Anomaly Detection (AD). When a one-class SVM model is applied, it produces a prediction and a probability for each case in the scoring data.

Building Anomaly Detection Models

Oracle Data Mining uses SVM as the one-class classifier for Anomaly Detection (AD).

When SVM is used for Anomaly Detection, it has the Classification mining function but no target.

To build an AD model, use an Anomaly Detection node connected to an appropriate data source.

Related Topics

- Support Vector Machine

- Anomaly Detection Node

Applying Anomaly Detection Models

Oracle Data Mining uses Support Vector Machine (SVM) as the one-class classifier for Anomaly Detection (AD). When a one-class SVM model is applied, it produces a prediction and a probability for each case in the scoring data.

- If the prediction is 1, then the case is considered typical.

- If the prediction is 0, then the case is considered anomalous.

This behavior reflects the fact that the model is trained with normal data.

Clustering

Clustering finds natural groupings of data objects, that is objects that are similar in some sense to one another.

The members of a cluster are more like each other than they are like members of other clusters. The goal of clustering analysis is to find high-quality clusters such that the inter-cluster similarity is low and the intra-cluster similarity is high.

The following topics discuss clustering:

- Using Clusters
You can use Clustering to segment data, to explore data, and also for anomaly detection.

- Calculating Clusters
Oracle Data Mining performs hierarchical clustering.

- Algorithms for Clustering
Oracle Data Mining supports these algorithms for clustering:

Using Clusters

You can use Clustering to segment data, to explore data, and also for anomaly detection.

Like Classification, use Clustering to segment data. Unlike Classification, Clustering models segment data into groups that were not previously defined. Classification models segment data by assigning it to previously defined classes, which are specified in a target. Clustering models do not use a target.

Clustering is useful for exploring data. If there are many cases and no obvious groupings, then you can use clustering algorithms to find natural groupings. Clustering can also serve as a useful data preprocessing step to identify homogeneous groups on which to build supervised models.

Clustering can also be used for anomaly detection. After the data has been segmented into clusters, you might find that some cases do not fit well into any clusters. These cases are anomalies or outliers.

Clusters are not necessarily disjoint; an item can be in several clusters.

Calculating Clusters

Oracle Data Mining performs hierarchical clustering.

The leaf clusters are the final clusters generated by the algorithm. Clusters higher up in the hierarchy are intermediate clusters.

Algorithms for Clustering

Oracle Data Mining supports these algorithms for clustering:

- k-Means Algorithm

- O-Cluster Algorithm

- Expectation Maximization. Requires Oracle Database 12c Release 1 (12.1) or later.

Association

Association rules express the relationships between items that take place at the same time.

Association rules are often used to analyze sales transactions. For example, it might be noted that customers who buy cereal at the grocery store often buy milk at the same time. In fact, association analysis might find that 85 percent of the checkout sessions that include cereal also include milk.

This application of association modeling is called market-basket analysis. It is valuable for direct marketing, sales promotions, and for discovering business trends. Market-basket analysis can also be used effectively for store layout, catalog design, and cross-sell.

Association modeling has important applications in other domains as well. For example, in e-commerce applications, association rules may be used for web page personalization. An association model might find that a user who visits pages A and B is 70 percent likely to also visit page C in the same session. Based on this rule, a dynamic link could be created for users who are likely to be interested in page C.

Association modeling analyzes data that consists of transactions.

- Transactions
In transactional data, a collection of items is associated with each case. A case consists of a transaction such as a market-basket or web session.

Transactions

In transactional data, a collection of items is associated with each case. A case consists of a transaction such as a market-basket or web session.

The collection of items in the transaction is an attribute of the transaction. Other attributes might be the date, time, location, or user ID associated with the transaction. However, in most cases, only a tiny subset of all possible items are present in a given transaction. The items in the market-basket represent only a small fraction of the items available for sale in the store. Association is transaction-based.

When an item is not present in a collection, it may have a null value or it may be missing. Many of the items may be missing or null, because many of the items that could be in the collection are probably not present in any individual transaction.

Feature Extraction and Selection

The Feature Extraction mining function combines attributes into a new reduced set of features. The Feature Selection mining function selects the most relevant attributes.

Sometimes too much information can reduce the effectiveness of data mining. Some columns of data attributes assembled for building and testing a model may not contribute meaningful information to the model. Some may actually detract from the quality and accuracy of the model.

Irrelevant attributes add noise to the data and affect model accuracy. Irrelevant attributes also increases the size of the model and the time and system resources needed for model building and scoring.

- Feature Selection
Feature Selection ranks the existing attributes according to their predictive significance

- Feature Extraction
Feature Extraction is an attribute reduction process.

Feature Selection

Feature Selection ranks the existing attributes according to their predictive significance

Finding the most significant predictors is the goal of some data mining projects. For example, a model might seek to find the principal characteristics of clients who pose a high credit risk.

Attribute Importance is also useful as a preprocessing step in classification modeling. Decision Tree and Generalized Linear Models benefit from this type of preprocessing. Oracle Data Mining implements Feature Selection for optimization within both of these algorithms

Oracle Data Miner provides the Attribute Importance setting in the Filter Columns node transformation to identify important features using the Oracle Data Mining importance function.

Related Topics

- Filter Columns Node

Feature Extraction

Feature Extraction is an attribute reduction process.

Unlike Feature Selection, which ranks the existing attributes according to their predictive significance, Feature Extraction actually transforms the attributes. The transformed attributes, or features, are linear combinations of the original attributes.

The Feature Extraction process results in a much smaller and richer set of attributes. The maximum number of features may be user-specified or determined by the algorithm. By default, it is determined by the algorithm.

Oracle Data Mining supports these algorithms for Feature Extraction:

- Nonnegative Matrix Factorization

- Singular Value Decomposition and Principal Components Analysis. Requires Oracle Database 12c Release 1 (12.1) or later.

9 Model Operations

Oracle Data Mining enables you to test Classification and Regression models.

A Test node is one of several ways to test a model. After you build a model, you apply the model to new data using an Apply node. Evaluate and Apply data must be prepared in the same way that build data was prepared.

The nodes related to model operations are:

- Apply Node
The Apply node takes a collection of models, both partitioned models and non-partitioned models, and returns a single score. The Apply node produces a query as the result.

- Feature Compare Node
The Feature Compare node allows you to perform calculations related to semantics in text data, contained in one Data Source node against another Data Source node.

- Test Node
Oracle Data Mining enables you to test Classification and Regression models. You cannot test other kinds of models.

Related Topics

- Evaluate and Apply Data

Apply Node

The Apply node takes a collection of models, both partitioned models and non-partitioned models, and returns a single score. The Apply node produces a query as the result.

The result can further transformed or connected to a Create Table or View node to save the data as a table. To make predictions using a model, you must apply the model to new data. This process is also called scoring the new data.

An Apply node generates the SQL for Scoring using one or more models. The SQL includes pass-through (supplemental) attributes and columns generated using Scoring functions.

Note:

You cannot apply Association or Attribute Importance models.

- Apply Preferences
In the Preferences dialog box, you can view and change preferences for Apply operations.

- Apply Node Input
Inputs for an Apply node can be Model nodes, Model Build nodes, or any node that generates data, such as Data node.

- Apply Node Output
An Apply node generates a data flow based on the Apply and Output specifications.

- Creating an Apply Node
You create an Apply node to score data based on the models.

- Apply and Output Specifications
There are several ways to create Apply and Output specifications for an Apply node.

- Evaluate and Apply Data
Test and Apply data for a model must be prepared in the same way that Build data for the model was prepared.

- Edit Apply Node
In the Edit Apply Node dialog box, you can specify or change the characteristics of the models to build.

- Apply Node Properties
In the Properties pane, you can examine and change the characteristics or properties of a node.

- Apply Node Context Menu
The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

- Apply Data Viewer
The Apply Data Viewer displays the data, columns, and the SQL queries used to generate the Apply output.

Related Topics

- About Parallel Processing

- About Oracle Database In-Memory

Apply Preferences

In the Preferences dialog box, you can view and change preferences for Apply operations.

To apply preferences to an Apply Node:

1. In Tools menu option, click Preferences.

2. In the Preferences dialog box, click Data Miner. You can view and change preferences for Apply operations. The default preferences for Data Miner are:

- Automatic Apply Settings

- Data Columns First

3. Click OK.

Apply Node Input

Inputs for an Apply node can be Model nodes, Model Build nodes, or any node that generates data, such as Data node.

An Apply node requires the following input:

- One or more of the following:

- Model node

- Model Build node

You must specify at least one model to apply. You can apply several models at the same time.

- Any node that generates data as an output such as a Data node, a Transforms node, or an appropriate Text node.
Only one input node is permitted.

When you apply a model to new data, the new data must be transformed in the same way as the data used to build the model.

Note:

You cannot apply Association or Attribute Importance models.

Apply Node Output

An Apply node generates a data flow based on the Apply and Output specifications.

You can provide specifications for Apply and Output in different ways such as using the Automatic Settings option, Define Apply Column Wizard and so on.

Related Topics

- Apply and Output Specifications

Creating an Apply Node

You create an Apply node to score data based on the models.

Before creating an Apply node, you must connect a Data node and a Model node or Build node to the Apply node.

To create an Apply node:

1. In the Components pane, go to Workflow Editor. If the Components pane is not visible, then in the SQL Developer menu bar, go to View and click Components. Alternately, press Ctrl+Shift+P to dock the Components pane.

2. Either identify Apply Data or create a Data Source node containing the Apply Data.

Note:
The Apply Data must be prepared in the same way as the Build Data.

3. Create a Model node, a Model Build node (such as a Classification node), or a combination of these nodes. At least one model must be successfully built before it can be applied. You cannot apply Association models.

4. In the Workflow Editor, expand Evaluate and Apply, and click Apply.

5. Drag and drop the node from the Components pane to the Workflow pane.
The node is added to the workflow. The GUI shows that the node has no data associated with it. Therefore, it cannot be run.

6. Link the Data node, Model nodes, and Build nodes to the Apply Node.

Related Topics

- Evaluate and Apply Data

- Data Source Node

- Model Node

- Classification Node

Apply and Output Specifications

There are several ways to create Apply and Output specifications for an Apply node.

You can choose to use any one of the following:

- Edit Apply Node
In the Edit Apply Node dialog box, you can edit settings for predictions, additional output, and automatic settings.

- Define Apply Columns Wizard
The Define Apply Column wizard is a two step wizard, that allows you to define Apply and Output specifications.

- Additional Output
Additional Output consists of columns that are passed unchanged through the Apply operation.

Edit Apply Node

In the Edit Apply Node dialog box, you can edit settings for predictions, additional output, and automatic settings.

To edit or view an Apply specification, either double-click the Apply node or right-click the Apply node and select Edit. The Edit Apply Node dialog box has two tabs:

- Predictions: Defines the Apply Scoring specifications.
An Apply specification consists of several Output Apply columns. The column names are generated automatically.

- You can specify names. The names must not be more than 30 characters.

- You can then select a model from the list of models in all Input nodes and an Apply function. The Apply functions that you can select depend on the selected models.

- Additional Output: Specifies pass-through columns from the Input node. You can select as many columns as you want. You can specify that these selected columns are displayed before the Apply columns (the default) or after the Apply columns.
These columns are often used to identify the Apply output. For example, you can use the Case ID column to identify the Apply output.

The default is to not specify any additional output.

The Default Column Order, at the bottom of the Edit Apply Node dialog box, is Data Columns First in the output. You can change this to Apply Columns First.

Select Order Partitions option if you want predictions for partitioned models. By default, this option is selected, even when there are non-partitioned models as inputs for the Apply node.

- Predictions
In the Predictions tab, you can define Apply Scoring specifications.

- Add or Edit Apply Output Column
In the Add Apply Output dialog box or the Edit apply Output dialog box, you can manually add or edit a single column Apply definition. You can edit or add Apply definitions one at a time.

- Add Output Apply Column Dialog
In the Add Output Apply Column dialog box, you can manually add and edit columns in a node that is connected to the Apply node.

Related Topics

- Apply Functions

- Apply Functions Parameters

- Additional Output

- Default Apply Column Names

Predictions

In the Predictions tab, you can define Apply Scoring specifications.

To define specific Apply settings or to edit the default settings, deselect Automatic Settings. You then add new Apply functions or edit existing ones.

Select a case ID from the Case ID drop-down list, as applicable.

You can edit settings in several ways:

- Show Partition Columns: Select a column and click to view the partition keys.

- Add a setting: Click to open the Add Output Apply Column dialog box.

- Edit an existing setting: Select the setting and click . The Edit Output Data Column dialog box opens.

- Delete a specification: Select it and click .

- Define Apply Columns: Click . In the Define Apply Columns Wizard, click the Define Apply Columns icon.

- Apply Functions
The Apply functions that you can choose depend on the models that you apply.

- Apply Functions Parameters

- Default Apply Column Names

Related Topics

- Define Apply Columns Wizard

- Add Output Apply Column Dialog

- Edit Output Data Column Dialog

Apply Functions

The Apply functions that you can choose depend on the models that you apply.

Note:

Certain Apply functions are available only if you are connected to Oracle Database 12c or later.

The Apply functions, arranged according to Model node are:

- Anomaly Detection Models

- Prediction: An automatic setting that returns the best prediction for the model. The data type returned depends on the target value type used during the build of the model. For Regression models, this function returns the expected value. The function returns the lowest cost prediction using the stored cost matrix if a cost matrix exists. If no stored cost matrix exists, then the function returns the highest probability prediction.

- Prediction Details: Returns prediction details. The return value describes the attributes of the prediction. For Anomaly Detection, the returned details refer to the highest probability class or the specified class value.

Note:

Prediction Details requires a connection to Oracle Database 12 c or later.

The defaults for Predictions Details are:

- Target Value: Most Likely

- Sort by Weights: Absolute value

- Maximum Length of Ranked Attribute List: 5

Prediction Details output is in XML format (XMLType data type). You must parse the output to find the data that you need.

- Prediction Probability: An automatic setting that returns the probability associated with the best prediction.

- Prediction Set: Returns a varray of objects containing all classes in a multiclass classification scenario. The object fields are named PREDICTION, PROBABILITY, and COST. The data type of the PREDICTION field depends on the target value type used during the build of the model. The other two fields are both Oracle NUMBER. The elements are returned in the order of best prediction to worst prediction.

- Clustering Models

- Cluster Details: The return value describes the attributes of the highest probability cluster or the specified cluster ID. If you specify a value for TopN, then the function returns the N attributes that most influence the cluster assignment (the score). If you do not specify TopN, then the function returns the five most influential attributes.

Note:

Cluster Details requires a connection to Oracle Database 12c or later.

The defaults for Predictions Details are as follows:

- Cluster ID: Most Likely

- Sort by Weight: Absolute value

- Maximum Length of Ranked Attribute List: 5

The returned attributes are ordered by weight. The weight of an attribute expresses its positive or negative impact on cluster assignment. A positive weight indicates an increased likelihood of assignment. A negative weight indicates a decreased likelihood of assignment.

Cluster Details output is in XML format (XMLType data type). You must parse the output to find the data that you need.

- Cluster Distance: Returns a cluster distance for each row in the selection. The cluster distance is the distance between the row and the centroid of the highest probability cluster or the specified cluster ID.

Note:

Cluster Distance requires connection to Oracle Database 12c. or later.

The defaults for Predictions Details are as follows:

- Cluster ID: Most Likely

- Cluster ID: An automatic setting that returns the NUMBER of the most probable cluster ID. If the cluster ID has been renamed, then a VARCHAR2 is returned instead.

- Cluster Probability: An automatic setting that returns a measure of the degree of confidence of membership (NUMBER) of an input row in a cluster associated with the specified model.

- Cluster Set: Returns an array of objects containing all possible clusters that a given row belongs to given the parameter specifications. Each object in the array is a pair of scalar values containing the cluster ID and the cluster probability. The object fields are named CLUSTER_ID and PROBABILITY, and both are Oracle NUMBER Clustering models only.

- Feature Extraction Models

- Feature ID: Returns an Oracle NUMBER that is the identifier of the feature with the highest value for the row.

- Feature Set: An automatic setting that is similar to Cluster Set.

- Feature Value: Returns the value of a given feature. If you omit the feature ID argument, then the function returns the highest feature value.

- Feature Details:  The return value describes the attributes of the highest value feature or the specified feature ID. If you specify a value for TopN, then the function returns the N attributes that most influence the feature value. If you do not specify TopN, then the function returns the 5 most influential attributes.

Note:

Feature Extraction Model requires connection to Oracle Database 12c or later.

The returned attributes are ordered by weight. The weight of an attribute expresses its positive or negative impact on the value of the feature. A positive weight indicates a higher feature value. A negative weight indicates a lower feature value.

The defaults for Predictions Details are as follows:

- Feature ID: Most Likely

- Sort by Weight: Absolute value

- Maximum Length of Ranked Attribute List: 5

Feature Details output is in XML format (XMLType data type). You must parse the output to find the data that you need.

- Classification and Regression Models

- Prediction: An automatic setting that returns the best prediction for the model. The data type returned depends on the target value type used during the build of the model.

- For Regression models, this function returns the expected value.

- For Classification models, the returned details refer to the highest probability class or the specified class value.
The function returns the lowest cost prediction using the stored cost matrix if a cost matrix exists. If no stored cost matrix exists, then the function returns the highest probability prediction.

- Prediction Bounds: For generalized linear models, it returns an object with two NUMBER fields LOWER and UPPER. If the GLM was built using Ridge Regression, or if the Covariance Matrix is found to be singular during the build, then this function returns NULL for both fields.

- For a Regression mining function, the bounds apply to value of the prediction.

- For a Classification mining function, the bounds apply to the probability value.

- Prediction Bounds Lower: Same as Prediction Bounds but only returns the lower bounds as a scalar column. Automatic Setting for GLM models.

- Prediction Bounds Upper: Same as Prediction Bounds but only returns the upper bounds as a scalar column. Automatic Setting for GLM models.

- Prediction Details: Requires connection to Oracle Database 12 c or later, except for Decision Tree.
The defaults for Predictions Details for Classification are as follows:

- Target Value: Most Likely

- Sort by Weights: Absolute value

- Maximum Length of Ranked Attribute List: 5

The defaults for Predictions Details for Regression are as follows:

- Sort by Weights: Absolute value

- Maximum Length of Ranked Attribute List: 5

DT Prediction Details: Returns a string containing model-specific information related to the scoring of the input row. In Oracle Data Miner releases earlier than 4.0, the return value is in the form <Node id = "integer"/>.

Note:

DT Prediction Details requires a connection to Oracle Database 11g Release 2 (11.2) or later.

- Classification

- Prediction Costs: Returns a measure of cost for a given prediction as a NUMBER. Classification models only. Automatic Setting for DT models.

- Prediction Probability: Returns the probability associated with the best prediction. The Automatic Setting for is Most Likely.

- Prediction Set: Returns an array of objects containing all classes in a multiclass classification scenario. The object fields are named PREDICTION, PROBABILITY, and COST. The data type of the PREDICTION field depends on the target value type used during the build of the model. The other two fields are both Oracle NUMBER. The elements are returned in the order of best prediction to worst prediction.

Related Topics

- Apply Functions Parameters

Apply Functions Parameters

The Apply Function parameters that can be specified:

- Cluster ID: The default is Most Probable. No other parameters are supported.

- Cluster Probability: The default is Most Probable. You can also select a specific cluster ID or specify NULL or Most Likely to return the bounds for the most likely cluster.

- Cluster Set: The default is All Clusters You can also specify either or both of the following:

- TopN: Where N is between one and the number of clusters. The optional TopN argument is a positive integer that restricts the set of features to those that have one of the top N values. If there is a tie at the Nth value, then the database still returns only N values. If you omit this argument, then the function returns all features.

- Probability Cutoff: It is a number strictly greater than zero and less than or equal to 1. The optional cutoff argument restricts the returned features to only those that have a feature value greater than or equal to the specified cutoff. To filter only by cutoff, specify Null for TopN and the desired cutoff for cutoff.

- Feature ID: The default is Most Probable. No other values are supported.

- Feature Set: The default is All Feature IDs. You can also specify either or both of the following:

- TopN: Where N is between 1 and the number of clusters. The optional TopN argument is a positive integer that restricts the set of features to those that have one of the top N values. If there is a tie at the Nth value, then the database still returns only N values. If you omit this argument, then the function returns all features.

- Probability Cutoff: It is a number strictly greater than zero and less than or equal to one. The optional cutoff argument restricts the returned features to only those that have a feature value greater than or equal to the specified cutoff. To filter only by cutoff, specify Null for TopN and the desired cutoff.

- Feature Value: The default is Highest Value. You can also select a specific feature ID value or specify anyone of the following value to return the bounds for the most likely feature:

- NULL

- Most Likely

- Prediction: The default is Best Prediction to consider the cost matrix.

- Prediction Upper Bounds or Prediction Lower Bounds: The default is Best Prediction with Confidence Level 95%. You can change Confidence Level to any number strictly greater than zero and less than or equal to one. For Classification models only, you can use the Target Value Selection dialog box option to pick a specific target value. You can also specify Null or Most Likely to return the bounds for the most likely target value.

- Prediction Costs: The default is Best Prediction. Applicable for Classification models only. You can use the Target Value Selection option to pick a specific target value.

- Prediction Details: Only value is the details for the Best Prediction.

- Prediction Probability: The default is Best Prediction. Applicable for Classification models only. You can use the Target Value Selection option to pick a specific target value.

- Prediction Set: The default is All Target Values. You can also specify one or both of the following:

- bestN: Where N is between one and the number of targets. The optional bestN argument is a positive integer that restricts the returned target classes to the N having the highest probability, or lowest cost if cost matrix clause is specified. If multiple classes are tied in the Nth value, then the database still returns only N values. To filter only by cutoff, specify Null for this parameter.

- Probability Cutoff: Is a number strictly greater than zero and less than or equal to one. The optional cutoff argument restricts the returned target classes to those with a probability greater than or equal to (or a cost less than or equal to if cost matrix clause is specified) the specified cutoff value. You can filter solely by cutoff by specifying Null for this value.

Default Apply Column Names

The syntax of the default Apply column name:

"<FUNCTION ABBREVIATION>_<MODEL NAME><SEQUENCE>

SEQUENCE is used only if necessary to avoid a conflict. A sequence number may force the model name to be partially truncated.

FUNCTION ABBREVIATION is one of the following:

- Cluster Details: CDET

- Cluster Distance: CDST

- Cluster ID: CLID

- Cluster Probability: PROB

- Cluster Set: CSET

- Feature Details: FDET

- Feature ID: FEID

- Feature Set: FSET

- Feature Value: FVAL

- Prediction: PRED

- Prediction Bounds: PBND

- Prediction Upper Bounds: PBUP

- Prediction Lower Bounds: PBLW

- Prediction Costs: PCST

- Prediction Details: PDET

- Prediction Probability: PROB

- Prediction Set: PSET

Specific target, feature, or cluster default names are abbreviated in one of two ways.

- The first approach attempts to integrate the value of the target, feature, or cluster into the column name. This approach is used if the maximum value of the target, cluster, or feature does not exceed the remaining character spaces available in the name. The name must be 30 or fewer characters.

- The second approach substitutes the target, cluster, or feature with a sequence ID. This approach is used if the first approach is not possible.

Add or Edit Apply Output Column

In the Add Apply Output dialog box or the Edit apply Output dialog box, you can manually add or edit a single column Apply definition. You can edit or add Apply definitions one at a time.

Before you add or edit columns, you must deselect Automatic Settings.

You can perform the following tasks:

- Add an Apply Output column: Click .

- Edit an Apply Output column: Click . When you edit a column, only the Function selection box and its parameters can be edited.

The following controls are available:

- Column: Name of column to be generated.

- Auto :

- If selected, then you cannot edit the name of the column.

- If deselected, then auto naming is disabled, and you can rename the column. Column names are validated to ensure that they are unique.

- Node: List of Model Input nodes connected to node. If there is only one Input node, then it is selected by default.

- Model: List of models for the selected node. If there is only one model, then it is selected by default.

- Function: List of model scoring functions for the selected model.

- Parameters: Displays 0 or more controls necessary to support the parameter requirements of the selected function.

When you have finished defining the output column, click OK.

Related Topics

- Default Apply Column Names

- Apply Functions

Add Output Apply Column Dialog

In the Add Output Apply Column dialog box, you can manually add and edit columns in a node that is connected to the Apply node.

By default, name is automatically assigned to the output column.

To add a column:

1. In the Column field, provide a name.

2. Deselect Auto.

3. In the Node field, select one of the node connected to the Apply node. The type of the node that you select determines the choices in the Model and Function fields.

4. In the Model field, select a model.

5. In the Function field, select a function.

6. After you are done, click OK.

Related Topics

- Apply and Output Specifications

Define Apply Columns Wizard

The Define Apply Column wizard is a two step wizard, that allows you to define Apply and Output specifications.

The Define Apply Column wizard comprises the following steps:

- Models
In the Models section in the Apply Columns wizard, you can select models and define the output specifications for it.

- Output Specifications
In Output Specifications section, the possible output specifications for the selected model are listed with the default settings selected.

- Define Top N
If you select the Top N option, then you must define the settings related to it.

Models

In the Models section in the Apply Columns wizard, you can select models and define the output specifications for it.

To select a model:

1. Select the Models for which you want to define output specification.

2. Click Next.

Output Specifications

In Output Specifications section, the possible output specifications for the selected model are listed with the default settings selected.

To define output specifications for the selected model:

1. Select Most Likely: To define the parameters under Most Likely:

- Select Prediction Details and click Edit to define the prediction details in the model.

- Select Prediction Bounds and enter the percentage for Confidence. This setting is applicable only for models based on the Generalized Linear Model algorithm.

2. Select Top N to define the N values in the Define Top N dialog box.

3. Select Partition Name if you want the name of the partitioned models in the output.

4. Click Finish to complete the definition.

Related Topics

- Apply and Output Specifications

- Define Top N

Define Top N

If you select the Top N option, then you must define the settings related to it.

To define the settings related to Top N option:

1. Click Use Best N and select the N Value from the drop-down list.

2. Click Cut Off and select a value from the Prob Value drop-down list.

3. Click OK.

Additional Output

Additional Output consists of columns that are passed unchanged through the Apply operation.

You can specify pass-through columns from the Input node. You can select as many columns as you want. You can specify that these selected columns are displayed before the Apply columns (the default) or after the Apply columns. These columns are often used to identify the Apply output. For example, you can use the Case ID column to identify the Apply output.

Related Topics

- Edit Output Data Column Dialog

Evaluate and Apply Data

Test and Apply data for a model must be prepared in the same way that Build data for the model was prepared.

To properly prepare Test and Apply data, duplicate the transformation chains of build data for Test and Apply data by copying and pasting build Transforms nodes.

Edit Apply Node

In the Edit Apply Node dialog box, you can specify or change the characteristics of the models to build.

The default value for Default Column Order is Data Columns First, which means that any data columns that you add come first in the output. You can change this to Apply Columns First.

Select Order Partitions option if you want predictions for partitioned models. By default, this option is selected, even when there are non-partitioned models as inputs for the Apply node.

The Edit Apply Node dialog box has the following tabs:

- Predictions
In the Predictions tab, you can define Apply Scoring specifications.

- Additional Output
In the Additional Output tab, you can specify pass-through attributes from Data Source nodes.

- Apply Columns
To create an Apply specification, deselect Automatic Settings. By default, Automatic Settings is selected.

Predictions

In the Predictions tab, you can define Apply Scoring specifications.

To define specific Apply settings or to edit the default settings, deselect Automatic Settings. You then add new Apply functions or edit existing ones.

Select a case ID from the Case ID drop-down list, as applicable.

You can edit settings in several ways:

- Show Partition Columns: Select a column and click to view the partition keys.

- Add a setting: Click to open the Add Output Apply Column dialog box.

- Edit an existing setting: Select the setting and click . The Edit Output Data Column dialog box opens.

- Delete a specification: Select it and click .

- Define Apply Columns: Click . In the Define Apply Columns Wizard, click the Define Apply Columns icon.

Related Topics

- Define Apply Columns Wizard

- Add Output Apply Column Dialog

- Edit Output Data Column Dialog

Additional Output

In the Additional Output tab, you can specify pass-through attributes from Data Source nodes.

To add columns:

1. Click . The Edit Output Data Column dialog box opens.

2. In the Edit Apply Node dialog box, the Default Column Order is Data Columns First. You can change this to Apply Columns First.

3. After you are done, click OK.

Related Topics

- Edit Output Data Column Dialog

Apply Columns

To create an Apply specification, deselect Automatic Settings. By default, Automatic Settings is selected.

You can perform the following tasks:

- To define Apply columns, click . The Define Apply Column wizard opens.

- To add an Output Apply column, click .
The Add Output Apply Column dialog box opens.

- To delete an Output Apply column, click .

- To edit an Output Apply column specification, select the specification. Click . The Add or Edit Apply Output Column dialog box opens.

Related Topics

- Add or Edit Apply Output Column

- Add Output Apply Column Dialog

Apply Node Properties

In the Properties pane, you can examine and change the characteristics or properties of a node.

To view the properties of a node, click the node and click Properties. If the Properties pane is closed, then go to View and click Properties. Alternately, right-click the node and click Go to Properties.

Apply Node Properties has the following sections:

- Predictions: Displays the Output Apply columns defined on the Apply Columns. You can edit these details. The option Automatic Selection is selected if selections were not modified.
For each Output Apply Column, the Name, Function, Parameters, and Node are listed.

- Additional Output: Lists the Output Data Columns that are passed through. For each column, the Name, Alias (if any), and Data Type are listed.

- Cache

- Details: Displays the name of the node and comments.

Related Topics

- Apply Columns

- Cache

Apply Node Context Menu

The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

To view the Apply node context menu, right-click the node. The following options are available in the context menu:

- Connect
Use the Connect option to link nodes in a workflow.

- Run
Use the Run option to execute the tasks specified in the nodes that comprise the workflow.

- Force Run
Use the Force Run option to rerun one or more nodes that are complete.

- Create Schedule
Use the Create Schedule option to define a schedule to run a workflow at a definite time and date.

- Edit
Use the Edit option to edit the default settings of a node.

- View Data
Use the View Data option to view the data contained in a Data node.

- Generate Apply Chain
Use the Generate Apply Chain to create a new node that contains the specification of a node that performs a transformation.

- Show Event Log
Use the Show Event Log option to view information about events in the current connection, errors, warnings, and information messages.

- Show Validation Errors
Use the Show Validation Errors option to view validation errors, if any.

- Validate Parents
Use the Validate Parents option to validate all parent nodes of the current node.

- Deploy
Use the Deploy option to deploy a node or workflow by creating SQL scripts that perform the tasks specified in the workflow.

- Save SQL
Use the Save SQL option to generate SQL script for the selected node.

- Cut
Use the Cut option to remove the selected object, which could be a node or connection.

- Copy
Use the Copy option to copy one or more nodes and paste them into the same workflow or a different workflow.

- Paste
Use the Paste option to paste the copied object in the workflow.

- Select All
Use the Select All option to select all the nodes in a workflow.

- Performance Settings
Use the Performance Settings option to edit Parallel settings and In-Memory settings of the nodes.

- Go to Properties
Use the Go to Properties option to open the Properties pane of the selected node.

- Navigate
Use the Navigate option to view the links available from the selected node.

Related Topics

- Apply Data Viewer

- Edit Apply Node

Connect

Use the Connect option to link nodes in a workflow.

To connect nodes:

1. Right-click a node and click Connect. Alternately, go to Diagram and click Connect.

2. Use the cursor to draw a line from this node to the target node.

3. Click to establish the connection. Note the following:

- You can create only valid connections.

- You can create only one connection between any two nodes.

- You can remove a connection by pressing the ESC key.

Related Topics

- Connect

Run

Use the Run option to execute the tasks specified in the nodes that comprise the workflow.

The Data Miner server runs workflows asynchronously. The client does not have to be connected. You can run one or more nodes in the workflow:

- To run one node: Right-click the node and select Run.

- To run multiple nodes simultaneously: Select the nodes by holding down the Ctrl key and click each individual node. Then right-click any selected node and select Run.

If a node depends on outputs of one or more parent nodes, then the parent node runs automatically only if the outputs required by the running node are missing.

Force Run

Use the Force Run option to rerun one or more nodes that are complete.

Force Run deletes any existing models before building them once again.

To select more than one node, click the nodes while holding down the Ctrl key.

You can Force Run a node at any location in a workflow. Depending on the location of the node in the workflow, you have the following choices for running the node using Force Run:

- Selected Node

- Selected Node and Children (available if the node has child nodes)

- Child Node Only (available if the node one or more child nodes)

- Selected Node and Parents (available if the node has parent nodes)

Create Schedule

Use the Create Schedule option to define a schedule to run a workflow at a definite time and date.

In the Create Schedule dialog box, you can create schedules for your workflows. To create workflow schedule:

1. Start Date: Select a date to set as the start date of the schedule. Click to select a date.

2. Repeat: Select any one of the following options:

- None: To schedule the workflow to run only once at the defined time.

- Every Day: To schedule the workflow to run daily at the specified time.

- Every Week: To schedule the workflow to run weekly at the specified time.

- Custom: To customize your workflow schedule, click Custom. This opens the Repeat dialog box, where you can set how frequently the workflow should run.

3. End Repeat: You can select any one of the following options:

- None: To continue running the workflow every hour.

- After: Select a number by clicking the arrows. This runs the workflow every hour, and would stop after the number of hours you have selected here. For example, if you select 8, then the workflow will run every hour, and after 8 hours, it will stop.

- On Date: Select a particular date by clicking the calendar icon.

4. Select Use Existing Schedule, and select a schedule from the drop-down list if you want to schedule the workflow as per the selected schedule.

- Click to edit the selected schedule in the Schedule dialog box.

- Click to add a new schedule. You can also edit the selected schedule, and add it here.

- Click to delete the selected schedule.

5. Click OK.

To save the workflow schedule settings, click . You can provide a name for the schedule in the Save a Schedule dialog box.

Related Topics

- Repeat

- Save a Schedule

Edit

Use the Edit option to edit the default settings of a node.

Nodes have default algorithms and settings. When you edit a node, the default algorithms and settings are modified. You can edit a node in any one of the following ways:

- Edit nodes using the Edit dialog box

- Edit nodes through Properties UI

Related Topics

- Editing Nodes through Edit Dialog Box

- Editing Nodes through Properties

View Data

Use the View Data option to view the data contained in a Data node.

The Data nodes are Create Table or View node, Data Source node, Explore Data node, Graph node, SQL Query node, and Update Table node.

Related Topics

- Data Source Node Viewer

Generate Apply Chain

Use the Generate Apply Chain to create a new node that contains the specification of a node that performs a transformation.

If you have several transformations performed in sequence, for example, Sample followed by a Custom transform, then you must select Generate Apply Chain for each transformation in the sequence.You must connect the individual nodes and connect them to an appropriate data source.

Generate Apply Chain helps you create a sequence of transformations that you can use to ensure that new data is prepared in the same way as existing data. For example, to ensure that Apply data is prepared in the same way as Build data, use this option.

The Generate Apply Chain option is not valid for all nodes. For example, it does not copy the specification of a Build node.

Show Event Log

Use the Show Event Log option to view information about events in the current connection, errors, warnings, and information messages.

Clicking the Show Event Log option opens the View and Event Log dialog box.

Related Topics

- View an Event Log

Show Validation Errors

Use the Show Validation Errors option to view validation errors, if any.

This option is displayed only when there are validation errors. For example, if an Association node is not connected to a Data Source node, then select Show Validation Errors to view the validation error No build data input node connected.

You can also view validation errors by moving the mouse over the node. The errors are displayed in a tool tip.

Validate Parents

Use the Validate Parents option to validate all parent nodes of the current node.

To validate parent nodes of a node, right-click the node and select Validate Parents.

You can validate parent nodes when the node is in Ready, Complete and Error state. All parent nodes must be in completed state.

Deploy

Use the Deploy option to deploy a node or workflow by creating SQL scripts that perform the tasks specified in the workflow.

The scripts generated by Deploy are saved to a directory.

Note:

You must run a node before deploying it.

You can generate a script that replicates the behavior of the entire workflow. Such a script can serve as the basis for application integration or as a light-weight deployment than the alternative of installing the Data Miner repository and workflows in the target and production system.

To deploy a workflow or part of a workflow:

1. Right-click a node and select Deploy.

2. Select any one of the deployment options:

- Selected node and dependent nodes

- Selected node, dependent nodes, and child nodes

- Selected node and connected nodes

3. After selecting the deployment option, the Generate SQL Script wizard opens. In the wizard, enter details for the following:

Related Topics

- Deploy Workflows using Object Generation Scripts

- Running Workflow Scripts

Save SQL

Use the Save SQL option to generate SQL script for the selected node.

To generate SQL script for the selected node:

1. Right-click the node and click Save SQL.

2. Select any one of the options to save the generated SQL script:

- SQL to Clipboard

- SQL to File

- SQL Script to Clipboard

- SQL Script to File

When you save to a file, the system provides a default location. You can browse to change this location. You can also create a folder for scripts.

The saved SQL includes SQL generated by the current node and all of its parent nodes that are data providers. The SQL lineage ends when it encounters a node that represents persisted objects, such as tables or models.

The generated script does not generate all behavior of the node. The script does not create any objects. For example, if you select Save SQLfor a Create Table node, then it does not generate a script to create the table. Instead, it generates a script to query the created table.

Related Topics

- Deploy Workflows using Data Query Scripts

Cut

Use the Cut option to remove the selected object, which could be a node or connection.

You can also delete objects by selecting them and pressing DELETE on your keyboard.

Copy

Use the Copy option to copy one or more nodes and paste them into the same workflow or a different workflow.

To copy and paste nodes:

1. Select the nodes to copy. To select several nodes, hold down the Ctrl key when you click the nodes.

The selected node is highlighted. In this example Classification is selected. The other node is not selected.

2. Right-click and select Copy from the context menu. Alternately, you can press Ctrl+C to copy the selected nodes.

Note:

Copying and pasting nodes do not carry any mining models or results from the original node.

Paste

Use the Paste option to paste the copied object in the workflow.

To paste an object, right-click the workflow and click Paste. Alternately, you can press Ctrl+V.

Note:

Node names and model names are changed to avoid naming collisions. To preserve names, use the option Extended Paste.

Related Topics

- Copy

- Extended Paste

Select All

Use the Select All option to select all the nodes in a workflow.

The selected nodes and links are highlighted in a dark blue border.

Performance Settings

Use the Performance Settings option to edit Parallel settings and In-Memory settings of the nodes.

If you click Performance Settings in the context menu, or if you click Performance Options in the workflow toolbar, then the Edit Selected Node Settings dialog box opens. It lists all the nodes that comprise the workflow. To edit the settings in the Edit Selected Node Settings dialog box:

- Click Parallel Settings and select:

- Enable: To enable parallel settings in the selected nodes in the workflow.

- Disable: To disable parallel settings in the selected nodes in the workflow.

- All: To turn on parallel processing for all nodes in the workflow.

- None: To turn off parallel processing for all nodes in the workflow.

- Click In-Memory Settings and select:

- Enable: To enable In-Memory settings for the selected nodes in the workflow.

- Disable: To disable In-Memory settings for the selected nodes in the workflow.

- All: To turn on In-Memory settings for the selected nodes in the workflow.

- None: To turn off In-Memory settings for all nodes in the workflow

- Click to set the Degree of Parallel, and In-Memory settings such as Compression Method, and Priority Levels in the Edit Node Performance Settings dialog box.
If you specify parallel settings for at least one node, then this indication appears in the workflow title bar:

Performance Settings is either On for Selected nodes, On (for All nodes), or Off. You can click Performance Options to open the Edit Selected Node Settings dialog box.

- Click to edit default the preferences for parallel processing.

- Edit Node Default Settings: You can edit the Parallel Settings and In-Memory settings for the selected node in the Performance Options dialog box. You can access the Performance Options dialog box from the Preferences options in the SQL Developer Tools menu.

- Change Settings to Default

Related Topics

- About Parallel Processing

- Edit Node Performance Settings

- Performance Options

Go to Properties

Use the Go to Properties option to open the Properties pane of the selected node.

Related Topics

- Managing Workflows and Nodes in the Properties Pane

Navigate

Use the Navigate option to view the links available from the selected node.

Note:
The Navigate option is enabled only if there are links to other nodes.

Navigate displays the collection of links available from this node. Selecting one of the links selects the link and the selected link is highlighted in the workflow. The link itself has context menu options as well so you can right click and continue with the Navigate option. You can also use the arrow keys to progress to the next node.

Apply Data Viewer

The Apply Data Viewer displays the data, columns, and the SQL queries used to generate the Apply output.

The Apply Data viewer opens in a new tab. The viewer has these tabs:

- Data: Displays rows of data. The default is to view the cache data. You can perform the following tasks:

- View actual data.

- Sort data.

- Filter data with a SQL expression.

- Refresh the display. To refresh, click .

- Columns: Lists the columns in the apply.

- SQL: Lists the SQL queries used to generate the Apply Output.

Feature Compare Node

The Feature Compare node allows you to perform calculations related to semantics in text data, contained in one Data Source node against another Data Source node.

The requirements of a Feature Compare node are:

- Two input data sources. The data source can be data flow of records, such as connected by a Data Source node or a single record data entered by user inside the node. In case of data entered by users, input data provider is not needed.

- One input Feature Extraction model provider node, where a model can be selected for calculations related to semantics.

You can do compare features of two data inputs sources by right-clicking the node and selecting Edit.

- Create Feature Compare Node
You create a Feature Compare node to perform calculations about text data.

- Feature Compare
In the Feature Compare dialog box, you can specify or change the characteristics of the models to build.

- Feature Compare Node Context Menu
The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

Create Feature Compare Node

You create a Feature Compare node to perform calculations about text data.

Before creating a Feature Compare node, first, create a workflow. Then, identify or create a Data Source node.

To create a Feature Compare node:

1. In the Components pane, go to Workflow Editor. If the Components pane is not visible, then in the SQL Developer menu bar, go to View and click Components. Alternately, press Ctrl+Shift+P to dock the Components pane.

2. In the Workflow Editor, expand Models, and click Feature Compare.

3. Drag and drop the node from the Components pane to the Workflow pane.
The node is added to the workflow. The GUI shows that the node has no data associated with it. Therefore, it cannot be run.

4. Move to the node that provides data for the build. Right-click and click Connect. Drag the line to the Feature Compare node and click again.

5. You can edit the node. To edit the node, right-click the node and click Edit. The Feature Compare dialog box opens.

6. The node is ready to build. Right-click the node and click Run.

Related Topics

- Feature Compare

Feature Compare

In the Feature Compare dialog box, you can specify or change the characteristics of the models to build.

In the Feature Compare dialog box, you can perform the following tasks.

- In the Feature Compare tab, you can select a Feature Extraction model and specify the data sources to be used for feature comparison. To specify data sources:

1. In the Model field, select a model from the drop down list. The drop-down list displays all the Feature Extraction models that are connected to the model provider.

2. Deselect Auto, if you want to enter a custom column name. If Auto is selected, then the Column field automatically displays the column name based on the selected model. The Auto option is for automatic column name generation.

3. In the Data Input 1 and Data Input 2 fields, select a data provider node from the drop-down list respectively. If you want to enter a custom input, then select User Defined from the drop-down list, and enter the custom entry by clicking the corresponding Data Input cell in the model grid below.

4. In the Case ID fields, select a supported column for each data provider node. If the Data Input field is set as User Defined, then the Case ID field is disabled.

5. Click OK.

The model grid displays the following:

- Model Attribute: Displays the input attributes from the model signature of the selected model.

- Data Type: Displays the attribute of the data type.

- Data Input 1: Displays the matching attribute or user defined data for Data Input 1.

- Data Input 2: Displays the matching attribute or user defined data for Data Input 1.

- In the Additional Outputs tab, the selected Case IDs added in the Feature Compare tab are also added here, when Automatic Setting is set to On. You can also add any model attributes as additional columns for output.

Feature Compare Node Context Menu

The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

To view the Feature Extraction node context menu, right-click the node. The following options are available in the context menu:

- Connect
Use the Connect option to link nodes in a workflow.

- Run
Use the Run option to execute the tasks specified in the nodes that comprise the workflow.

- Force Run
Use the Force Run option to rerun one or more nodes that are complete.

- Create Schedule
Use the Create Schedule option to define a schedule to run a workflow at a definite time and date.

- Edit
Use the Edit option to edit the default settings of a node.

- View Data
Use the View Data option to view the data contained in a Data node.

- Generate Apply Chain
Use the Generate Apply Chain to create a new node that contains the specification of a node that performs a transformation.

- Show Event Log
Use the Show Event Log option to view information about events in the current connection, errors, warnings, and information messages.

- Show Validation Errors
Use the Show Validation Errors option to view validation errors, if any.

- Validate Parents
Use the Validate Parents option to validate all parent nodes of the current node.

- Deploy
Use the Deploy option to deploy a node or workflow by creating SQL scripts that perform the tasks specified in the workflow.

- Save SQL
Use the Save SQL option to generate SQL script for the selected node.

- Cut
Use the Cut option to remove the selected object, which could be a node or connection.

- Copy
Use the Copy option to copy one or more nodes and paste them into the same workflow or a different workflow.

- Paste
Use the Paste option to paste the copied object in the workflow.

- Select All
Use the Select All option to select all the nodes in a workflow.

- Performance Settings
Use the Performance Settings option to edit Parallel settings and In-Memory settings of the nodes.

- Go to Properties
Use the Go to Properties option to open the Properties pane of the selected node.

- Navigate
Use the Navigate option to view the links available from the selected node.

Related Topics

- Feature Compare

- Publish Diagram

Connect

Use the Connect option to link nodes in a workflow.

To connect nodes:

1. Right-click a node and click Connect. Alternately, go to Diagram and click Connect.

2. Use the cursor to draw a line from this node to the target node.

3. Click to establish the connection. Note the following:

- You can create only valid connections.

- You can create only one connection between any two nodes.

- You can remove a connection by pressing the ESC key.

Related Topics

- Connect

Run

Use the Run option to execute the tasks specified in the nodes that comprise the workflow.

The Data Miner server runs workflows asynchronously. The client does not have to be connected. You can run one or more nodes in the workflow:

- To run one node: Right-click the node and select Run.

- To run multiple nodes simultaneously: Select the nodes by holding down the Ctrl key and click each individual node. Then right-click any selected node and select Run.

If a node depends on outputs of one or more parent nodes, then the parent node runs automatically only if the outputs required by the running node are missing.

Force Run

Use the Force Run option to rerun one or more nodes that are complete.

Force Run deletes any existing models before building them once again.

To select more than one node, click the nodes while holding down the Ctrl key.

You can Force Run a node at any location in a workflow. Depending on the location of the node in the workflow, you have the following choices for running the node using Force Run:

- Selected Node

- Selected Node and Children (available if the node has child nodes)

- Child Node Only (available if the node one or more child nodes)

- Selected Node and Parents (available if the node has parent nodes)

Create Schedule

Use the Create Schedule option to define a schedule to run a workflow at a definite time and date.

In the Create Schedule dialog box, you can create schedules for your workflows. To create workflow schedule:

1. Start Date: Select a date to set as the start date of the schedule. Click to select a date.

2. Repeat: Select any one of the following options:

- None: To schedule the workflow to run only once at the defined time.

- Every Day: To schedule the workflow to run daily at the specified time.

- Every Week: To schedule the workflow to run weekly at the specified time.

- Custom: To customize your workflow schedule, click Custom. This opens the Repeat dialog box, where you can set how frequently the workflow should run.

3. End Repeat: You can select any one of the following options:

- None: To continue running the workflow every hour.

- After: Select a number by clicking the arrows. This runs the workflow every hour, and would stop after the number of hours you have selected here. For example, if you select 8, then the workflow will run every hour, and after 8 hours, it will stop.

- On Date: Select a particular date by clicking the calendar icon.

4. Select Use Existing Schedule, and select a schedule from the drop-down list if you want to schedule the workflow as per the selected schedule.

- Click to edit the selected schedule in the Schedule dialog box.

- Click to add a new schedule. You can also edit the selected schedule, and add it here.

- Click to delete the selected schedule.

5. Click OK.

To save the workflow schedule settings, click . You can provide a name for the schedule in the Save a Schedule dialog box.

Related Topics

- Repeat

- Save a Schedule

Edit

Use the Edit option to edit the default settings of a node.

Nodes have default algorithms and settings. When you edit a node, the default algorithms and settings are modified. You can edit a node in any one of the following ways:

- Edit nodes using the Edit dialog box

- Edit nodes through Properties UI

Related Topics

- Editing Nodes through Edit Dialog Box

- Editing Nodes through Properties

View Data

Use the View Data option to view the data contained in a Data node.

The Data nodes are Create Table or View node, Data Source node, Explore Data node, Graph node, SQL Query node, and Update Table node.

Related Topics

- Data Source Node Viewer

Generate Apply Chain

Use the Generate Apply Chain to create a new node that contains the specification of a node that performs a transformation.

If you have several transformations performed in sequence, for example, Sample followed by a Custom transform, then you must select Generate Apply Chain for each transformation in the sequence.You must connect the individual nodes and connect them to an appropriate data source.

Generate Apply Chain helps you create a sequence of transformations that you can use to ensure that new data is prepared in the same way as existing data. For example, to ensure that Apply data is prepared in the same way as Build data, use this option.

The Generate Apply Chain option is not valid for all nodes. For example, it does not copy the specification of a Build node.

Show Event Log

Use the Show Event Log option to view information about events in the current connection, errors, warnings, and information messages.

Clicking the Show Event Log option opens the View and Event Log dialog box.

Related Topics

- View an Event Log

Show Validation Errors

Use the Show Validation Errors option to view validation errors, if any.

This option is displayed only when there are validation errors. For example, if an Association node is not connected to a Data Source node, then select Show Validation Errors to view the validation error No build data input node connected.

You can also view validation errors by moving the mouse over the node. The errors are displayed in a tool tip.

Validate Parents

Use the Validate Parents option to validate all parent nodes of the current node.

To validate parent nodes of a node, right-click the node and select Validate Parents.

You can validate parent nodes when the node is in Ready, Complete and Error state. All parent nodes must be in completed state.

Deploy

Use the Deploy option to deploy a node or workflow by creating SQL scripts that perform the tasks specified in the workflow.

The scripts generated by Deploy are saved to a directory.

Note:

You must run a node before deploying it.

You can generate a script that replicates the behavior of the entire workflow. Such a script can serve as the basis for application integration or as a light-weight deployment than the alternative of installing the Data Miner repository and workflows in the target and production system.

To deploy a workflow or part of a workflow:

1. Right-click a node and select Deploy.

2. Select any one of the deployment options:

- Selected node and dependent nodes

- Selected node, dependent nodes, and child nodes

- Selected node and connected nodes

3. After selecting the deployment option, the Generate SQL Script wizard opens. In the wizard, enter details for the following:

Related Topics

- Deploy Workflows using Object Generation Scripts

- Running Workflow Scripts

Save SQL

Use the Save SQL option to generate SQL script for the selected node.

To generate SQL script for the selected node:

1. Right-click the node and click Save SQL.

2. Select any one of the options to save the generated SQL script:

- SQL to Clipboard

- SQL to File

- SQL Script to Clipboard

- SQL Script to File

When you save to a file, the system provides a default location. You can browse to change this location. You can also create a folder for scripts.

The saved SQL includes SQL generated by the current node and all of its parent nodes that are data providers. The SQL lineage ends when it encounters a node that represents persisted objects, such as tables or models.

The generated script does not generate all behavior of the node. The script does not create any objects. For example, if you select Save SQLfor a Create Table node, then it does not generate a script to create the table. Instead, it generates a script to query the created table.

Related Topics

- Deploy Workflows using Data Query Scripts

Cut

Use the Cut option to remove the selected object, which could be a node or connection.

You can also delete objects by selecting them and pressing DELETE on your keyboard.

Copy

Use the Copy option to copy one or more nodes and paste them into the same workflow or a different workflow.

To copy and paste nodes:

1. Select the nodes to copy. To select several nodes, hold down the Ctrl key when you click the nodes.

The selected node is highlighted. In this example Classification is selected. The other node is not selected.

2. Right-click and select Copy from the context menu. Alternately, you can press Ctrl+C to copy the selected nodes.

Note:

Copying and pasting nodes do not carry any mining models or results from the original node.

Paste

Use the Paste option to paste the copied object in the workflow.

To paste an object, right-click the workflow and click Paste. Alternately, you can press Ctrl+V.

Note:

Node names and model names are changed to avoid naming collisions. To preserve names, use the option Extended Paste.

Related Topics

- Copy

- Extended Paste

Select All

Use the Select All option to select all the nodes in a workflow.

The selected nodes and links are highlighted in a dark blue border.

Performance Settings

Use the Performance Settings option to edit Parallel settings and In-Memory settings of the nodes.

If you click Performance Settings in the context menu, or if you click Performance Options in the workflow toolbar, then the Edit Selected Node Settings dialog box opens. It lists all the nodes that comprise the workflow. To edit the settings in the Edit Selected Node Settings dialog box:

- Click Parallel Settings and select:

- Enable: To enable parallel settings in the selected nodes in the workflow.

- Disable: To disable parallel settings in the selected nodes in the workflow.

- All: To turn on parallel processing for all nodes in the workflow.

- None: To turn off parallel processing for all nodes in the workflow.

- Click In-Memory Settings and select:

- Enable: To enable In-Memory settings for the selected nodes in the workflow.

- Disable: To disable In-Memory settings for the selected nodes in the workflow.

- All: To turn on In-Memory settings for the selected nodes in the workflow.

- None: To turn off In-Memory settings for all nodes in the workflow

- Click to set the Degree of Parallel, and In-Memory settings such as Compression Method, and Priority Levels in the Edit Node Performance Settings dialog box.
If you specify parallel settings for at least one node, then this indication appears in the workflow title bar:

Performance Settings is either On for Selected nodes, On (for All nodes), or Off. You can click Performance Options to open the Edit Selected Node Settings dialog box.

- Click to edit default the preferences for parallel processing.

- Edit Node Default Settings: You can edit the Parallel Settings and In-Memory settings for the selected node in the Performance Options dialog box. You can access the Performance Options dialog box from the Preferences options in the SQL Developer Tools menu.

- Change Settings to Default

Related Topics

- About Parallel Processing

- Edit Node Performance Settings

- Performance Options

Go to Properties

Use the Go to Properties option to open the Properties pane of the selected node.

Related Topics

- Managing Workflows and Nodes in the Properties Pane

Navigate

Use the Navigate option to view the links available from the selected node.

Note:
The Navigate option is enabled only if there are links to other nodes.

Navigate displays the collection of links available from this node. Selecting one of the links selects the link and the selected link is highlighted in the workflow. The link itself has context menu options as well so you can right click and continue with the Navigate option. You can also use the arrow keys to progress to the next node.

Test Node

Oracle Data Mining enables you to test Classification and Regression models. You cannot test other kinds of models.

A Test node can test several models using the same test set. If the option Automatic Settings is set to ON, then the Test node specification is generated when you connect the input nodes.

A Test node can run in parallel.

Note:

All models tested in a node must be either Classification or Regression Models. You cannot test both kinds of models in the same test node.

- Support for Testing Classification and Regression Models
Oracle Data Miner supports testing of a Classification or Regression models.

- Test Node Input
The input for a Test node can be can be a Model node, a Classification node, or a Regression node.

- Automatic Settings
By default, the option Automatic Settings is selected for a Test node.

- Creating a Test Node
You create a test node to test Classification and Regression models.

- Edit Test Node
In the Edit Test Node dialog box, you can specify or change the characteristics of the models to build.

- Compare Test Results Viewer
The Compare Test Result viewer displays test results for one or more models in the same node.

- Test Node Properties
In the Properties pane, you can examine and change the characteristics or properties of a node.

- Test Node Context Menu
The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

Related Topics

- Automatic Settings

- About Parallel Processing

- Test Node Input

Support for Testing Classification and Regression Models

Oracle Data Miner supports testing of a Classification or Regression models.

Oracle Data Miner supports testing of a Classification or Regression models in the following ways:

- Test the model as part of the Build node using any one of the following ways:

- Split the Build data into build and test subsets.

- Use all of the Build data as test data.

- Connect a second Data Source node, the test Data Source node to the Build node

- Test the model in a Test node. In this case, the test data can be any table that is compatible with the Build data.

- After you have tested a Classification Model, you can tune it.

Note:

You cannot tune Regression models.

Related Topics

- Evaluate and Apply Data

- Tuning Classification Models

- Test

Test Node Input

The input for a Test node can be can be a Model node, a Classification node, or a Regression node.

A Test node has the following input:

- At least one node that identifies one or more models. The nodes can be a Model node, a Classification node, or a Regression node. A Model node must contain either Classification or Regression model, but not both.

- Any node that generates data as an output such as a Data node, a Transform node, or an appropriate Text node. This node contains the test data.

- It is recommended that a case ID is specified. If you do not specify a Case ID, then the processing will take longer.

You can test several Classification or several Regression Models at the same time. The models to be tested can be in different nodes. The models to be tested must satisfy these conditions:

- The nodes that contain the models must have the same function type. That is, they must be all Classification Build nodes or all Regression Build nodes.
Classification Models must also have the same list of target attribute values.

- The models must have the same target attribute with the same data type.

- The Data Source node for Test must contain the target of the models.

- The test data must be compatible with the models. That is, it should have been transformed in the same way as the data used to build the model.

Automatic Settings

By default, the option Automatic Settings is selected for a Test node.

Automatic Settings result in the following behavior:

- When a model input node is connected, all models are added to the specification.

- When a model input node is disconnected, all models are removed from the specification. The test node may become invalid.

- When a model input node is edited in the following ways, the resultant behavior is as follows:

- If models are added, then model specifications are automatically added to the Test node.

- If models are removed, then the specifications are removed from the Test node.

- If models are changed, then the following is done:

- The Test node is updated to ensure the algorithm is consistent.

- If the target changes and there is only one node as input to the Test node, then the node is updated to reflect the new target and keep all the models. Also, the test input data is validated to ensure that it still has the new column target.

- If there are multiple Model nodes as input to the Test nodes, then the models with the changed target are automatically removed.

If Automatic Settings is deselected, then you must edit the node to reflect all changes to the input. Models are validated if they are added.

Creating a Test Node

You create a test node to test Classification and Regression models.

Before you create a Test node, you must connect a Data Source node to a Model node or Build node to the Test node.

To create a Test node:

1. In the Components pane, go to Workflow Editor. If the Components pane is not visible, then in the SQL Developer menu bar, go to View and click Components. Alternately, press Ctrl+Shift+P to dock the Components pane.

2. Either identify or create a Data Source node containing the test data. Ensure that the test data is prepared in the same way as the build data.

3. Select at least one Model node, Classification node, or Regression node. A model must be successfully built before it can be tested.

Note:

You can test either Classification or Regression models but not both kinds of models in one Test node.

4. In the Workflow Editor, expand Evaluate and Apply, and click Test.

5. Drag and drop the node from the Components pane to the Workflow pane.
The node is added to the workflow. The GUI shows that the node has no data associated with it. Therefore, it cannot be run.

6. Link the Data node, the Model node or Build node to the Test Node.

7. Characteristics of the Test node are set by default. You can also edit the node.

Related Topics

- Evaluate and Apply Data

- Classification Node

- Regression Node

Edit Test Node

In the Edit Test Node dialog box, you can specify or change the characteristics of the models to build.

To edit the Test Node, right-click the node and select Edit or double-click the node. The Edit Test Node dialog box opens.

The Edit Test Node dialog box displays the following:

- Function (CLASSIFICATION or REGRESSION)

- Target and the Data Type (data type of the target)

- The Case ID (if there is one)
It is recommended that you specify a case ID. If you do not specify a case ID, then processing will be slower. The case ID that you specify for the Text node should be the same as the case ID specified for the Build node.

- Automatic Settings: By default, Automatic Settings is selected.

You can perform the following tasks:

- Compare test results and view individual models even when Automatic Settings is selected. The models tested are listed in the Selected Models grid.

- Make change to the list of the models. Deselect Automatic Settings and make changes in the Selected Models grid.

- Select Model

Related Topics

- No Case ID

- Automatic Settings

Select Model

The Select Model dialog box lists the models that are available for testing. To select models:

1. Move the Models from Available Models to Selected Models.

2. Click OK.

Compare Test Results Viewer

The Compare Test Result viewer displays test results for one or more models in the same node.

The following test results are displayed:

- Compare Classification Test Results

- Compare Regression Test Results

Related Topics

- Classification Model Test Viewer

- Regression Model Test Viewer

Test Node Properties

In the Properties pane, you can examine and change the characteristics or properties of a node.

To view the properties of a node, click the node and click Properties. If the Properties pane is closed, then go to View and click Properties. Alternately, right-click the node and click Go to Properties.

The Test node Properties pane has these sections:

- Models
The Models tab lists the models to test in the Selected Models grid.

- Test
The Test section describes how testing is performed.

- Details
The Details section displays the node name and comments about the node.

Models

The Models tab lists the models to test in the Selected Models grid.

- Selected Models
The Selected Models dialog box displays the details of the selected model. You can also add and delete models here.

Selected Models

The Selected Models dialog box displays the details of the selected model. You can also add and delete models here.

For each model, the grid lists the following:

- Model Name: Lists the model names. The partitioned models have the icon next to it to indicate that they are partitioned.

Note:

If the Partitioned columns are incompatible among the selected models, then only global test results are generated.

- Partition Columns: Lists the partition columns for each partitioned model.

- Node: Lists the node containing the model.

- Test: Indicates the test status of the model.

- Algorithm: Lists the algorithm that is used to build the model.

You can perform the following tasks:

- View Partition Column: Click to view the details of the partitioned columns in the selected model. The name, data type and source of the partitioned columns are displayed in the Partition Columns Definition dialog box.

- Add Model: Click . You can only add models that have the same function. Before adding a model, deselect Automatic Settings.

- Delete Model: Select it and click . Before deleting a model, deselect Automatic Settings.

Related Topics

- Compare Test Results Viewer

Test

The Test section describes how testing is performed.

Test contains these information:

- Function: CLASSIFICATION or REGRESSION.

- Target: The name of the target.

- Data Type: The data type of the target

- For CLASSIFICATION, these test results are calculated by default:

- Performance Metrics

- ROC Curve (Binary Target Only)

- Lift and Profit

You can deselect Metrics.

By default, the top 100 target values by frequency is specified. To change this value, click Edit. Edit the value in the Target Values Selection dialog box.

- For REGRESSION, Accuracy Matrix and Residuals are selected. You can deselect Metrics.

- The Performance Metrics are the metrics displayed on the Performance tab of the Test Viewer.

- Residuals are displayed on the Residual tab of the Test Viewer.

Related Topics

- Target Values Selection

- Residual

- Test Metrics for Classification Models

- Performance (Regression)

Details

The Details section displays the node name and comments about the node.

You can change the name of the node and edit the comments in this section. The new node name and comments must meet the requirements.

Related Topics

- Node Name and Node Comments

Test Node Context Menu

The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

To view the Test node context menu, right-click the node. The following options are available in the context menu:

- Connect

- Edit. Opens the Edit Test Node dialog box.

- Validate Parents

- Run

- Force Run

- View Models

- View Test Results

- Compare Test Results. Opens the Compare Test Results dialog box.

- Generate Apply Chain

- Cut

- Copy

- Paste

- Select All

- Performance Settings. This opens the Edit Selected Node Settings dialog box, where you can set Parallel Settings and In-Memory settings for the node.

- Navigate

- Show Runtime Errors. Displayed only if there is an error.

- Show Validation Errors. Displayed only if there are validation errors.

- Show Event Log. Displayed only if there is an error.

Related Topics

- Compare Test Results Viewer

- Edit Test Node

10 Predictive Query Nodes

Predictive Query nodes enable you to score data dynamically without a predefined model. Predictive Queries use in-database scoring technology.

Note:

Predictive Query Nodes require Oracle 12c Release 1 or later.

Scoring using Predictive Query nodes has the following limitations:

- The transient models created during the running of Predictive Query node are not available for inspection or fine tuning.

- If it is necessary to inspect the model, correlate scoring results with the model, specify special algorithm settings, or run multiple scoring queries that use the same model, then a predefined model must be created.

The output of a Predictive Query is the output of an Apply operation.

There are several Predictive Query nodes:

- Anomaly Detection Query
An Anomaly Detection Query node analyses the input for anomalies. That is, it detects unusual cases in data.

- Clustering Query
A Clustering Query node returns the clusters in the input.

- Feature Extraction Query
A Feature Extraction Query extracts features from the input.

- Prediction Query
A Prediction Query node performs classification and regression using the input.

Related Topics

- Apply Node Output

Anomaly Detection Query

An Anomaly Detection Query node analyses the input for anomalies. That is, it detects unusual cases in data.

Note:

Predictive Query Nodes require Oracle 12c Release 1 or later.

Anomaly Detection Query can run in parallel.

- Create an Anomaly Detection Query Node
You create an Anomaly Detection Query node to build an Anomaly Detection model to analyze and detect anomalous occurrences such as fraud.

- Edit an Anomaly Detection Query
In the an Anomaly Detection Query Node dialog box, you can specify or change the characteristics of the models to build.

- Anomaly Detection Query Properties
In the Properties pane, you can examine and change the characteristics or properties of a node.

- Anomaly Detection Query Context Menu
The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

Related Topics

- Anomaly Detection

- About Parallel Processing

Create an Anomaly Detection Query Node

You create an Anomaly Detection Query node to build an Anomaly Detection model to analyze and detect anomalous occurrences such as fraud.

To create an Anomaly Detection Query in an existing workflow:

1. In the Components pane, go to Workflow Editor. If the Components pane is not visible, then in the SQL Developer menu bar, go to View and click Components. Alternately, press Ctrl+Shift+P to dock the Components pane.

2. Create the Data Source node containing the input data.

3. Expand the Predictive Query section in the Components pane.

4. Drag and drop the node from the Components pane to the Workflow pane.
The node is added to the workflow. The GUI shows that the node has no data associated with it. Therefore, it cannot be run.

5. Connect the Data Source node to the Anomaly Detection Query Node.

6. Edit the Anomaly Detection Query node.

7. Run the Predictive Query node and view the data for a Predictive Query node.

8. To save the results of the query, use a Create Table or View node.

Related Topics

- Run Predictive Query Node

- View Data for a Predictive Query

- Edit an Anomaly Detection Query

Edit an Anomaly Detection Query

In the an Anomaly Detection Query Node dialog box, you can specify or change the characteristics of the models to build.

To edit an Anomaly Detection Query node:

1. Double-click the Anomaly Detection Query node or right-click the node and click Edit. The Edit Anomaly Detection Query Node dialog box opens.

2. In the Anomaly Detection Query Node dialog box, enter the details in the following tabs:

- Anomaly Predictions tab:

- In the Case ID field, select a case ID from the drop-down list (Optional). It is recommended that you specify a case ID to uniquely define each record. The case ID helps with model repeatability and is consistent with good data mining practices.

- You can edit the outputs of an Anomaly Prediction (optional). Oracle Data Miner automatically determines the output for the query. You can modify the output.

- Partition tab: You can perform the following tasks.

- Add one or more Partition attributes. This is optional. Selecting a Partition attribute directs the predictive query to build a virtual model for each unique partition.

- Select partitions. To select partitions, click the Partition tab and click . Use the Add Partitioning Columns dialog box to select the partitions. You can also specify partitioning expressions.

- Input tab: You can perform the following tasks:

- Modify Input

- Add and modify input. Click Input to add and modify input.

- Remove input.

- Change the mining type.

- Additional Output tab: You can add output (optional). By default, all target columns, the Case ID column, and partitioning columns are automatically added to additional output. To make changes, click Additional Output.

3. Click OK.

- Edit Anomaly Prediction Output
Oracle Data Miner automatically selects output for query. You can select and edit the parameters of the output functions.

- Add Anomaly Function
You can add an anomaly function in the Anomaly Function dialog box.

- Edit Anomaly Function Dialog
You can edit the anomaly function in the Edit Anomaly Function dialog box.

Related Topics

- Add Additional Output

- Add Partitioning Columns

- Modify Input

Edit Anomaly Prediction Output

Oracle Data Miner automatically selects output for query. You can select and edit the parameters of the output functions.

The default output is listed in the Anomaly Prediction Outputs section in the Anomaly Predictions tab. The defaults are:

- Prediction

- Prediction Details

- Prediction Probability

You can select Prediction Set and edit parameters of the output functions. You can perform the following tasks:

- Delete: To delete an output, select the output and click .

- Add: To add an output, click . Use the Add Anomaly Function dialog box to select an output.

- Edit: To edit an output, either double-click the function or select the function and click . Use Edit Anomaly Function dialog box to make changes.

The output of a Predictive Query is the output of an Apply (Scoring) operation.

Related Topics

- Apply Functions

- Add Anomaly Function

- Edit Anomaly Function Dialog

Add Anomaly Function

You can add an anomaly function in the Anomaly Function dialog box.

To add anomaly function:

1. In the Function field, select a function from the drop-down list. The options are:

- Prediction

- Prediction Probability

- Prediction Details

- Prediction Set

2. To specify a default name instead of using the default name, deselect Auto. This turns off automatic selection.

3. Click OK.

Edit Anomaly Function Dialog

You can edit the anomaly function in the Edit Anomaly Function dialog box.

To edit an anomaly function:

1. Select the change that you want to make to the function.

2. To specify a name instead of using the default name, deselect Auto. This turns off automatic selection.

3. Click OK.

Anomaly Detection Query Properties

In the Properties pane, you can examine and change the characteristics or properties of a node.

To view the properties of a node, click the node and click Properties. If the Properties pane is closed, then go to View and click Properties. Alternately, right-click the node and click Go to Properties.

The Anomaly Detection Query Node properties has these sections:

- Anomaly Predictions: Displays the predictions produced by the query.

- Partition

- Additional Output: Displays the output specified.

- Cache

- Details

Anomaly Detection Query Context Menu

The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

To view the Anomaly Detection Query node context menu, right-click the node. The following options are available in the context menu:

- Connect
Use the Connect option to link nodes in a workflow.

- Run
Use the Run option to execute the tasks specified in the nodes that comprise the workflow.

- Force Run
Use the Force Run option to rerun one or more nodes that are complete.

- Create Schedule
Use the Create Schedule option to define a schedule to run a workflow at a definite time and date.

- Edit
Use the Edit option to edit the default settings of a node.

- View Data
Use the View Data option to view the data contained in a Data node.

- Generate Apply Chain
Use the Generate Apply Chain to create a new node that contains the specification of a node that performs a transformation.

- Show Event Log
Use the Show Event Log option to view information about events in the current connection, errors, warnings, and information messages.

- Deploy
Use the Deploy option to deploy a node or workflow by creating SQL scripts that perform the tasks specified in the workflow.

- Cut
Use the Cut option to remove the selected object, which could be a node or connection.

- Copy
Use the Copy option to copy one or more nodes and paste them into the same workflow or a different workflow.

- Paste
Use the Paste option to paste the copied object in the workflow.

- Extended Paste
Use the Extended Paste option to preserve node and model names while pasting them.

- Select All
Use the Select All option to select all the nodes in a workflow.

- Performance Settings
Use the Performance Settings option to edit Parallel settings and In-Memory settings of the nodes.

- Toolbar Actions
Use the Toolbar Action option to select actions in the toolbar from the context menu.

- Show Runtime Errors
Use the Show Runtime Errors to view errors related to node failure during runtime. This option is displayed only when running of the node fails at runtime.

- Show Validation Errors
Use the Show Validation Errors option to view validation errors, if any.

- Save SQL
Use the Save SQL option to generate SQL script for the selected node.

- Validate Parents
Use the Validate Parents option to validate all parent nodes of the current node.

- Go to Properties
Use the Go to Properties option to open the Properties pane of the selected node.

Related Topics

- Edit an Anomaly Detection Query

- View Data for a Predictive Query

Connect

Use the Connect option to link nodes in a workflow.

To connect nodes:

1. Right-click a node and click Connect. Alternately, go to Diagram and click Connect.

2. Use the cursor to draw a line from this node to the target node.

3. Click to establish the connection. Note the following:

- You can create only valid connections.

- You can create only one connection between any two nodes.

- You can remove a connection by pressing the ESC key.

Related Topics

- Connect

Run

Use the Run option to execute the tasks specified in the nodes that comprise the workflow.

The Data Miner server runs workflows asynchronously. The client does not have to be connected. You can run one or more nodes in the workflow:

- To run one node: Right-click the node and select Run.

- To run multiple nodes simultaneously: Select the nodes by holding down the Ctrl key and click each individual node. Then right-click any selected node and select Run.

If a node depends on outputs of one or more parent nodes, then the parent node runs automatically only if the outputs required by the running node are missing.

Force Run

Use the Force Run option to rerun one or more nodes that are complete.

Force Run deletes any existing models before building them once again.

To select more than one node, click the nodes while holding down the Ctrl key.

You can Force Run a node at any location in a workflow. Depending on the location of the node in the workflow, you have the following choices for running the node using Force Run:

- Selected Node

- Selected Node and Children (available if the node has child nodes)

- Child Node Only (available if the node one or more child nodes)

- Selected Node and Parents (available if the node has parent nodes)

Create Schedule

Use the Create Schedule option to define a schedule to run a workflow at a definite time and date.

In the Create Schedule dialog box, you can create schedules for your workflows. To create workflow schedule:

1. Start Date: Select a date to set as the start date of the schedule. Click to select a date.

2. Repeat: Select any one of the following options:

- None: To schedule the workflow to run only once at the defined time.

- Every Day: To schedule the workflow to run daily at the specified time.

- Every Week: To schedule the workflow to run weekly at the specified time.

- Custom: To customize your workflow schedule, click Custom. This opens the Repeat dialog box, where you can set how frequently the workflow should run.

3. End Repeat: You can select any one of the following options:

- None: To continue running the workflow every hour.

- After: Select a number by clicking the arrows. This runs the workflow every hour, and would stop after the number of hours you have selected here. For example, if you select 8, then the workflow will run every hour, and after 8 hours, it will stop.

- On Date: Select a particular date by clicking the calendar icon.

4. Select Use Existing Schedule, and select a schedule from the drop-down list if you want to schedule the workflow as per the selected schedule.

- Click to edit the selected schedule in the Schedule dialog box.

- Click to add a new schedule. You can also edit the selected schedule, and add it here.

- Click to delete the selected schedule.

5. Click OK.

To save the workflow schedule settings, click . You can provide a name for the schedule in the Save a Schedule dialog box.

Related Topics

- Repeat

- Save a Schedule

Edit

Use the Edit option to edit the default settings of a node.

Nodes have default algorithms and settings. When you edit a node, the default algorithms and settings are modified. You can edit a node in any one of the following ways:

- Edit nodes using the Edit dialog box

- Edit nodes through Properties UI

Related Topics

- Editing Nodes through Edit Dialog Box

- Editing Nodes through Properties

View Data

Use the View Data option to view the data contained in a Data node.

The Data nodes are Create Table or View node, Data Source node, Explore Data node, Graph node, SQL Query node, and Update Table node.

Related Topics

- Data Source Node Viewer

Generate Apply Chain

Use the Generate Apply Chain to create a new node that contains the specification of a node that performs a transformation.

If you have several transformations performed in sequence, for example, Sample followed by a Custom transform, then you must select Generate Apply Chain for each transformation in the sequence.You must connect the individual nodes and connect them to an appropriate data source.

Generate Apply Chain helps you create a sequence of transformations that you can use to ensure that new data is prepared in the same way as existing data. For example, to ensure that Apply data is prepared in the same way as Build data, use this option.

The Generate Apply Chain option is not valid for all nodes. For example, it does not copy the specification of a Build node.

Show Event Log

Use the Show Event Log option to view information about events in the current connection, errors, warnings, and information messages.

Clicking the Show Event Log option opens the View and Event Log dialog box.

Related Topics

- View an Event Log

Deploy

Use the Deploy option to deploy a node or workflow by creating SQL scripts that perform the tasks specified in the workflow.

The scripts generated by Deploy are saved to a directory.

Note:

You must run a node before deploying it.

You can generate a script that replicates the behavior of the entire workflow. Such a script can serve as the basis for application integration or as a light-weight deployment than the alternative of installing the Data Miner repository and workflows in the target and production system.

To deploy a workflow or part of a workflow:

1. Right-click a node and select Deploy.

2. Select any one of the deployment options:

- Selected node and dependent nodes

- Selected node, dependent nodes, and child nodes

- Selected node and connected nodes

3. After selecting the deployment option, the Generate SQL Script wizard opens. In the wizard, enter details for the following:

Related Topics

- Deploy Workflows using Object Generation Scripts

- Running Workflow Scripts

Cut

Use the Cut option to remove the selected object, which could be a node or connection.

You can also delete objects by selecting them and pressing DELETE on your keyboard.

Copy

Use the Copy option to copy one or more nodes and paste them into the same workflow or a different workflow.

To copy and paste nodes:

1. Select the nodes to copy. To select several nodes, hold down the Ctrl key when you click the nodes.

The selected node is highlighted. In this example Classification is selected. The other node is not selected.

2. Right-click and select Copy from the context menu. Alternately, you can press Ctrl+C to copy the selected nodes.

Note:

Copying and pasting nodes do not carry any mining models or results from the original node.

Paste

Use the Paste option to paste the copied object in the workflow.

To paste an object, right-click the workflow and click Paste. Alternately, you can press Ctrl+V.

Note:

Node names and model names are changed to avoid naming collisions. To preserve names, use the option Extended Paste.

Related Topics

- Copy

- Extended Paste

Extended Paste

Use the Extended Paste option to preserve node and model names while pasting them.

The default behavior of Paste is to change node names and model names to avoid naming collisions.

To go to the Extended Paste option, right-click the workflow and click Extended Paste. Alternately, you can press Control+Shift+V.

Note:

If model names are not unique, then the models may be overwritten when they are rebuilt.

Related Topics

- Copy

Select All

Use the Select All option to select all the nodes in a workflow.

The selected nodes and links are highlighted in a dark blue border.

Performance Settings

Use the Performance Settings option to edit Parallel settings and In-Memory settings of the nodes.

If you click Performance Settings in the context menu, or if you click Performance Options in the workflow toolbar, then the Edit Selected Node Settings dialog box opens. It lists all the nodes that comprise the workflow. To edit the settings in the Edit Selected Node Settings dialog box:

- Click Parallel Settings and select:

- Enable: To enable parallel settings in the selected nodes in the workflow.

- Disable: To disable parallel settings in the selected nodes in the workflow.

- All: To turn on parallel processing for all nodes in the workflow.

- None: To turn off parallel processing for all nodes in the workflow.

- Click In-Memory Settings and select:

- Enable: To enable In-Memory settings for the selected nodes in the workflow.

- Disable: To disable In-Memory settings for the selected nodes in the workflow.

- All: To turn on In-Memory settings for the selected nodes in the workflow.

- None: To turn off In-Memory settings for all nodes in the workflow

- Click to set the Degree of Parallel, and In-Memory settings such as Compression Method, and Priority Levels in the Edit Node Performance Settings dialog box.
If you specify parallel settings for at least one node, then this indication appears in the workflow title bar:

Performance Settings is either On for Selected nodes, On (for All nodes), or Off. You can click Performance Options to open the Edit Selected Node Settings dialog box.

- Click to edit default the preferences for parallel processing.

- Edit Node Default Settings: You can edit the Parallel Settings and In-Memory settings for the selected node in the Performance Options dialog box. You can access the Performance Options dialog box from the Preferences options in the SQL Developer Tools menu.

- Change Settings to Default

Related Topics

- About Parallel Processing

- Edit Node Performance Settings

- Performance Options

Toolbar Actions

Use the Toolbar Action option to select actions in the toolbar from the context menu.

Current actions are Zoom In and Zoom Out.

Related Topics

- Managing Workflows using Workflow Controls

Show Runtime Errors

Use the Show Runtime Errors to view errors related to node failure during runtime. This option is displayed only when running of the node fails at runtime.

The Event Log opens with a list of errors. Select the error to see the exact message and details.

Related Topics

- View an Event Log

Show Validation Errors

Use the Show Validation Errors option to view validation errors, if any.

This option is displayed only when there are validation errors. For example, if an Association node is not connected to a Data Source node, then select Show Validation Errors to view the validation error No build data input node connected.

You can also view validation errors by moving the mouse over the node. The errors are displayed in a tool tip.

Save SQL

Use the Save SQL option to generate SQL script for the selected node.

To generate SQL script for the selected node:

1. Right-click the node and click Save SQL.

2. Select any one of the options to save the generated SQL script:

- SQL to Clipboard

- SQL to File

- SQL Script to Clipboard

- SQL Script to File

When you save to a file, the system provides a default location. You can browse to change this location. You can also create a folder for scripts.

The saved SQL includes SQL generated by the current node and all of its parent nodes that are data providers. The SQL lineage ends when it encounters a node that represents persisted objects, such as tables or models.

The generated script does not generate all behavior of the node. The script does not create any objects. For example, if you select Save SQLfor a Create Table node, then it does not generate a script to create the table. Instead, it generates a script to query the created table.

Related Topics

- Deploy Workflows using Data Query Scripts

Validate Parents

Use the Validate Parents option to validate all parent nodes of the current node.

To validate parent nodes of a node, right-click the node and select Validate Parents.

You can validate parent nodes when the node is in Ready, Complete and Error state. All parent nodes must be in completed state.

Go to Properties

Use the Go to Properties option to open the Properties pane of the selected node.

Related Topics

- Managing Workflows and Nodes in the Properties Pane

Clustering Query

A Clustering Query node returns the clusters in the input.

Note:

Predictive Query nodes require Oracle 12c Release 1 or later.

A Clustering Query can run in parallel.

- Create a Clustering Query
You create a Clustering Node to build clustering models.

- Edit a Clustering Query
In the Edit Clustering Query Node dialog box, you can specify or change the characteristics of the models to build.

- Clustering Query Properties
In the Properties pane, you can examine and change the characteristics or properties of a node.

- Clustering Query Context Menu
The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

Related Topics

- Clustering

- About Parallel Processing

- About Oracle Database In-Memory

Create a Clustering Query

You create a Clustering Node to build clustering models.

To create a Clustering Query in an existing workflow:

1. In the Components pane, go to Workflow Editor. If the Components pane is not visible, then in the SQL Developer menu bar, go to View and click Components. Alternately, press Ctrl+Shift+P to dock the Components pane.

2. Create a Data Source node containing the input data.

3. Expand the Predictive Queries section in the Components pane.

4. Drag and drop the node from the Components pane to the Workflow pane.
The node is added to the workflow. The GUI shows that the node has no data associated with it. Therefore, it cannot be run.

5. Connect the Data Source node to the Clustering Query node.

6. Edit the Clustering Query node.

7. Run the Predictive Query node and view the data.

8. To save the results of the query, use a Create Table or View node.

Related Topics

- Edit a Clustering Query

- Run Predictive Query Node

- View Data for a Predictive Query

Edit a Clustering Query

In the Edit Clustering Query Node dialog box, you can specify or change the characteristics of the models to build.

To edit a Clustering Query node:

1. Double-click the Clustering Query node or right-click the node and click Edit. The Edit Clustering Query Node dialog box opens.

2. In the Edit Clustering Query Node dialog box, enter the details in the following tabs:

- Cluster Predictions tab: In the Case ID field, select a case ID from the drop-down list. The case ID is optional. It is recommended that you specify a Case ID to uniquely define each record. The case ID helps with model repeatability and is consistent with good data mining practices.

- In the Case ID field, select a case ID from the drop-down list. The case ID is optional. It is recommended that you specify a case ID to uniquely define each record. The case ID helps with model repeatability and is consistent with good data mining practices.

- In the Number of Clusters to Compute field, specify the number to compute. Default is 10.

- Edit Cluster Prediction Outputs. Oracle Data Miner automatically determines the output for the query. You can modify the output.

- Partition tab: You can perform the following tasks:

- Add one or more Partition attributes. This is optional. Selecting a Partition attribute directs the predictive query to build a virtual model for each unique partition.

- Select Partitions: To select partitions, click the Partitions tab. Then, click . Then use the Add Partitioning Columns to select the partitions. You can also specify partitioning expressions.

- Input tab: You can modify Input. This is optional. You can add or remove inputs and change the mining types of inputs. Click Input.

- Additional Output tab: You can add outputs (optional). By default, all target columns, the Case ID column, and partitioning columns are automatically added to Additional Output. To make changes, click Additional Output.

3. Click OK.

- Edit Cluster Prediction Outputs
Oracle Data Miner automatically selects output for query. The default outputs are listed in the Cluster Prediction Outputs section in the Cluster Predictions tab.

- Add Cluster Function
In the Add Cluster Function dialog box, you can add cluster functions.

- Edit Cluster Function
In the Edit Cluster Function dialog box, you can edit the function.

Related Topics

- Add Partitioning Columns

- Add Additional Output

- Modify Input

Edit Cluster Prediction Outputs

Oracle Data Miner automatically selects output for query. The default outputs are listed in the Cluster Prediction Outputs section in the Cluster Predictions tab.

The output of a Predictive Query is the output of an Apply (scoring) operation. The defaults are:

- Cluster Details

- Cluster Distance

- Cluster ID

- Cluster Probability

You can also select Cluster Set, and edit parameters of the output functions. You can perform the following tasks:

- Delete: To delete an output, select the output and click .

- Add: To add an output, click . Use Add Cluster Function dialog box to select an output.

- Edit: To edit an output, either double-click the function or select the function and click . Use the Edit Cluster Function dialog box to make changes.

Related Topics

- Apply Functions

- Add Cluster Function

- Edit Cluster Function

Add Cluster Function

In the Add Cluster Function dialog box, you can add cluster functions.

To add Cluster function:

1. In the Function field, select a function from the drop-down list. The options are:

- Cluster ID

- Cluster Probability

- Cluster Details

- Cluster Distance

- Cluster Set

2. To specify a default name instead of using the default name, deselect Auto. This turns off automatic selection.

3. Click OK.

Edit Cluster Function

In the Edit Cluster Function dialog box, you can edit the function.

To edit Cluster function:

1. Select the change that you want to make to the function.

2. To specify a name instead of using the default name, deselect Auto. This turns off automatic selection.

3. Click OK.

Clustering Query Properties

In the Properties pane, you can examine and change the characteristics or properties of a node.

To view the properties of a node, click the node and click Properties. If the Properties pane is closed, then go to View and click Properties. Alternately, right-click the node and click Go to Properties.

The Clustering Query Properties pane has these sections:

- Cluster Predictions: Displays the predictions produced by the query.

- Partition

- Additional Output: Displays the output specified.

- Cache

- Details

Clustering Query Context Menu

The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

To view the Clustering Query node context menu, right-click the node. The following options are available in the context menu:

- Connect
Use the Connect option to link nodes in a workflow.

- Run
Use the Run option to execute the tasks specified in the nodes that comprise the workflow.

- Force Run
Use the Force Run option to rerun one or more nodes that are complete.

- Create Schedule
Use the Create Schedule option to define a schedule to run a workflow at a definite time and date.

- Edit
Use the Edit option to edit the default settings of a node.

- View Data
Use the View Data option to view the data contained in a Data node.

- Generate Apply Chain
Use the Generate Apply Chain to create a new node that contains the specification of a node that performs a transformation.

- Show Event Log
Use the Show Event Log option to view information about events in the current connection, errors, warnings, and information messages.

- Deploy
Use the Deploy option to deploy a node or workflow by creating SQL scripts that perform the tasks specified in the workflow.

- Cut
Use the Cut option to remove the selected object, which could be a node or connection.

- Copy
Use the Copy option to copy one or more nodes and paste them into the same workflow or a different workflow.

- Paste
Use the Paste option to paste the copied object in the workflow.

- Extended Paste
Use the Extended Paste option to preserve node and model names while pasting them.

- Select All
Use the Select All option to select all the nodes in a workflow.

- Performance Settings
Use the Performance Settings option to edit Parallel settings and In-Memory settings of the nodes.

- Toolbar Actions
Use the Toolbar Action option to select actions in the toolbar from the context menu.

- Show Runtime Errors
Use the Show Runtime Errors to view errors related to node failure during runtime. This option is displayed only when running of the node fails at runtime.

- Show Validation Errors
Use the Show Validation Errors option to view validation errors, if any.

- Save SQL
Use the Save SQL option to generate SQL script for the selected node.

- Validate Parents
Use the Validate Parents option to validate all parent nodes of the current node.

- Go to Properties
Use the Go to Properties option to open the Properties pane of the selected node.

Connect

Use the Connect option to link nodes in a workflow.

To connect nodes:

1. Right-click a node and click Connect. Alternately, go to Diagram and click Connect.

2. Use the cursor to draw a line from this node to the target node.

3. Click to establish the connection. Note the following:

- You can create only valid connections.

- You can create only one connection between any two nodes.

- You can remove a connection by pressing the ESC key.

Related Topics

- Connect

Run

Use the Run option to execute the tasks specified in the nodes that comprise the workflow.

The Data Miner server runs workflows asynchronously. The client does not have to be connected. You can run one or more nodes in the workflow:

- To run one node: Right-click the node and select Run.

- To run multiple nodes simultaneously: Select the nodes by holding down the Ctrl key and click each individual node. Then right-click any selected node and select Run.

If a node depends on outputs of one or more parent nodes, then the parent node runs automatically only if the outputs required by the running node are missing.

Force Run

Use the Force Run option to rerun one or more nodes that are complete.

Force Run deletes any existing models before building them once again.

To select more than one node, click the nodes while holding down the Ctrl key.

You can Force Run a node at any location in a workflow. Depending on the location of the node in the workflow, you have the following choices for running the node using Force Run:

- Selected Node

- Selected Node and Children (available if the node has child nodes)

- Child Node Only (available if the node one or more child nodes)

- Selected Node and Parents (available if the node has parent nodes)

Create Schedule

Use the Create Schedule option to define a schedule to run a workflow at a definite time and date.

In the Create Schedule dialog box, you can create schedules for your workflows. To create workflow schedule:

1. Start Date: Select a date to set as the start date of the schedule. Click to select a date.

2. Repeat: Select any one of the following options:

- None: To schedule the workflow to run only once at the defined time.

- Every Day: To schedule the workflow to run daily at the specified time.

- Every Week: To schedule the workflow to run weekly at the specified time.

- Custom: To customize your workflow schedule, click Custom. This opens the Repeat dialog box, where you can set how frequently the workflow should run.

3. End Repeat: You can select any one of the following options:

- None: To continue running the workflow every hour.

- After: Select a number by clicking the arrows. This runs the workflow every hour, and would stop after the number of hours you have selected here. For example, if you select 8, then the workflow will run every hour, and after 8 hours, it will stop.

- On Date: Select a particular date by clicking the calendar icon.

4. Select Use Existing Schedule, and select a schedule from the drop-down list if you want to schedule the workflow as per the selected schedule.

- Click to edit the selected schedule in the Schedule dialog box.

- Click to add a new schedule. You can also edit the selected schedule, and add it here.

- Click to delete the selected schedule.

5. Click OK.

To save the workflow schedule settings, click . You can provide a name for the schedule in the Save a Schedule dialog box.

Related Topics

- Repeat

- Save a Schedule

Edit

Use the Edit option to edit the default settings of a node.

Nodes have default algorithms and settings. When you edit a node, the default algorithms and settings are modified. You can edit a node in any one of the following ways:

- Edit nodes using the Edit dialog box

- Edit nodes through Properties UI

Related Topics

- Editing Nodes through Edit Dialog Box

- Editing Nodes through Properties

View Data

Use the View Data option to view the data contained in a Data node.

The Data nodes are Create Table or View node, Data Source node, Explore Data node, Graph node, SQL Query node, and Update Table node.

Related Topics

- Data Source Node Viewer

Generate Apply Chain

Use the Generate Apply Chain to create a new node that contains the specification of a node that performs a transformation.

If you have several transformations performed in sequence, for example, Sample followed by a Custom transform, then you must select Generate Apply Chain for each transformation in the sequence.You must connect the individual nodes and connect them to an appropriate data source.

Generate Apply Chain helps you create a sequence of transformations that you can use to ensure that new data is prepared in the same way as existing data. For example, to ensure that Apply data is prepared in the same way as Build data, use this option.

The Generate Apply Chain option is not valid for all nodes. For example, it does not copy the specification of a Build node.

Show Event Log

Use the Show Event Log option to view information about events in the current connection, errors, warnings, and information messages.

Clicking the Show Event Log option opens the View and Event Log dialog box.

Related Topics

- View an Event Log

Deploy

Use the Deploy option to deploy a node or workflow by creating SQL scripts that perform the tasks specified in the workflow.

The scripts generated by Deploy are saved to a directory.

Note:

You must run a node before deploying it.

You can generate a script that replicates the behavior of the entire workflow. Such a script can serve as the basis for application integration or as a light-weight deployment than the alternative of installing the Data Miner repository and workflows in the target and production system.

To deploy a workflow or part of a workflow:

1. Right-click a node and select Deploy.

2. Select any one of the deployment options:

- Selected node and dependent nodes

- Selected node, dependent nodes, and child nodes

- Selected node and connected nodes

3. After selecting the deployment option, the Generate SQL Script wizard opens. In the wizard, enter details for the following:

Related Topics

- Deploy Workflows using Object Generation Scripts

- Running Workflow Scripts

Cut

Use the Cut option to remove the selected object, which could be a node or connection.

You can also delete objects by selecting them and pressing DELETE on your keyboard.

Copy

Use the Copy option to copy one or more nodes and paste them into the same workflow or a different workflow.

To copy and paste nodes:

1. Select the nodes to copy. To select several nodes, hold down the Ctrl key when you click the nodes.

The selected node is highlighted. In this example Classification is selected. The other node is not selected.

2. Right-click and select Copy from the context menu. Alternately, you can press Ctrl+C to copy the selected nodes.

Note:

Copying and pasting nodes do not carry any mining models or results from the original node.

Paste

Use the Paste option to paste the copied object in the workflow.

To paste an object, right-click the workflow and click Paste. Alternately, you can press Ctrl+V.

Note:

Node names and model names are changed to avoid naming collisions. To preserve names, use the option Extended Paste.

Related Topics

- Copy

- Extended Paste

Extended Paste

Use the Extended Paste option to preserve node and model names while pasting them.

The default behavior of Paste is to change node names and model names to avoid naming collisions.

To go to the Extended Paste option, right-click the workflow and click Extended Paste. Alternately, you can press Control+Shift+V.

Note:

If model names are not unique, then the models may be overwritten when they are rebuilt.

Related Topics

- Copy

Select All

Use the Select All option to select all the nodes in a workflow.

The selected nodes and links are highlighted in a dark blue border.

Performance Settings

Use the Performance Settings option to edit Parallel settings and In-Memory settings of the nodes.

If you click Performance Settings in the context menu, or if you click Performance Options in the workflow toolbar, then the Edit Selected Node Settings dialog box opens. It lists all the nodes that comprise the workflow. To edit the settings in the Edit Selected Node Settings dialog box:

- Click Parallel Settings and select:

- Enable: To enable parallel settings in the selected nodes in the workflow.

- Disable: To disable parallel settings in the selected nodes in the workflow.

- All: To turn on parallel processing for all nodes in the workflow.

- None: To turn off parallel processing for all nodes in the workflow.

- Click In-Memory Settings and select:

- Enable: To enable In-Memory settings for the selected nodes in the workflow.

- Disable: To disable In-Memory settings for the selected nodes in the workflow.

- All: To turn on In-Memory settings for the selected nodes in the workflow.

- None: To turn off In-Memory settings for all nodes in the workflow

- Click to set the Degree of Parallel, and In-Memory settings such as Compression Method, and Priority Levels in the Edit Node Performance Settings dialog box.
If you specify parallel settings for at least one node, then this indication appears in the workflow title bar:

Performance Settings is either On for Selected nodes, On (for All nodes), or Off. You can click Performance Options to open the Edit Selected Node Settings dialog box.

- Click to edit default the preferences for parallel processing.

- Edit Node Default Settings: You can edit the Parallel Settings and In-Memory settings for the selected node in the Performance Options dialog box. You can access the Performance Options dialog box from the Preferences options in the SQL Developer Tools menu.

- Change Settings to Default

Related Topics

- About Parallel Processing

- Edit Node Performance Settings

- Performance Options

Toolbar Actions

Use the Toolbar Action option to select actions in the toolbar from the context menu.

Current actions are Zoom In and Zoom Out.

Related Topics

- Managing Workflows using Workflow Controls

Show Runtime Errors

Use the Show Runtime Errors to view errors related to node failure during runtime. This option is displayed only when running of the node fails at runtime.

The Event Log opens with a list of errors. Select the error to see the exact message and details.

Related Topics

- View an Event Log

Show Validation Errors

Use the Show Validation Errors option to view validation errors, if any.

This option is displayed only when there are validation errors. For example, if an Association node is not connected to a Data Source node, then select Show Validation Errors to view the validation error No build data input node connected.

You can also view validation errors by moving the mouse over the node. The errors are displayed in a tool tip.

Save SQL

Use the Save SQL option to generate SQL script for the selected node.

To generate SQL script for the selected node:

1. Right-click the node and click Save SQL.

2. Select any one of the options to save the generated SQL script:

- SQL to Clipboard

- SQL to File

- SQL Script to Clipboard

- SQL Script to File

When you save to a file, the system provides a default location. You can browse to change this location. You can also create a folder for scripts.

The saved SQL includes SQL generated by the current node and all of its parent nodes that are data providers. The SQL lineage ends when it encounters a node that represents persisted objects, such as tables or models.

The generated script does not generate all behavior of the node. The script does not create any objects. For example, if you select Save SQLfor a Create Table node, then it does not generate a script to create the table. Instead, it generates a script to query the created table.

Related Topics

- Deploy Workflows using Data Query Scripts

Validate Parents

Use the Validate Parents option to validate all parent nodes of the current node.

To validate parent nodes of a node, right-click the node and select Validate Parents.

You can validate parent nodes when the node is in Ready, Complete and Error state. All parent nodes must be in completed state.

Go to Properties

Use the Go to Properties option to open the Properties pane of the selected node.

Related Topics

- Managing Workflows and Nodes in the Properties Pane

Feature Extraction Query

A Feature Extraction Query extracts features from the input.

Note:

Predictive Query nodes require Oracle 12c Release 1 or later.

A Feature Extraction Query node can run in parallel.

- Create a Feature Extraction Query
You create a Feature Extraction Query node to extract features from the data source or input.

- Edit Feature Extraction Query
In the Feature Extraction Query Node dialog box, you can specify or change the characteristics of the models to build.

- Feature Extraction Query Properties
In the Properties pane, you can examine and change the characteristics or properties of a node.

- Feature Extraction Query Context Menu
The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

Related Topics

- Feature Extraction

- Feature Extraction and Selection

- About Parallel Processing

- About Oracle Database In-Memory

Create a Feature Extraction Query

You create a Feature Extraction Query node to extract features from the data source or input.

To create a Feature Extraction Query node to an existing workflow:

1. In the Components pane, go to Workflow Editor. If the Components pane is not visible, then in the SQL Developer menu bar, go to View and click Components. Alternately, press Ctrl+Shift+P to dock the Components pane.

2. Create a Data Source node containing the input data.

3. Expand the Predictive Queries section in the Components pane.

4. Drag and drop the node from the Components pane to the Workflow pane.
The node is added to the workflow. The GUI shows that the node has no data associated with it. Therefore, it cannot be run.

5. Connect the Data Source node to the Feature Extraction Query node.

6. Edit the Feature Extraction Query node.

7. Run the Predictive Query node and view the data.

8. To save the results of the query, use a Create Table or View node.

Related Topics

- Edit Feature Extraction Query

- Run Predictive Query Node

- View Data for a Predictive Query

Edit Feature Extraction Query

In the Feature Extraction Query Node dialog box, you can specify or change the characteristics of the models to build.

To edit a Feature Extraction Query Node:

1. Double-click the Feature Extraction Query node or right-click the node and click Edit. The Edit Feature Extraction Query Node dialog box opens.

2. In the Feature Extraction Query Node dialog box, enter the details in the following tabs:

- Feature Predictions tab:

- In the Case ID field, select a case ID from the drop-down list. The case ID is optional. It is recommended that you specify a case ID to uniquely define each record. The case ID helps with model repeatability and is consistent with good data mining practices.

- In the Number of Features to Extract field, specify an input. The default is 10.

- Edit Feature Prediction Outputs. Oracle Data Miner automatically determines the output for the query. You can modify the output.

- Partitions tab:

- Add one or more Partition attributes. This is optional. Selecting a Partition attribute directs the predictive query to build a virtual model for each unique partition. To add partitions, click in the Partitions tab. Use the Add Partitioning Columns dialog box.

- Specify partitioning expressions.

- Input tab: You can perform the following tasks:

- Add input

- Modify input

- Change the mining type

- Additional Output tab: You can add output. This is optional. By default, all target columns, the Case ID column, and partitioning columns are automatically added to Additional Output. To make changes, click Additional Output.

3. Click OK.

- Edit Feature Prediction Outputs
The output of a Predictive Query is the output of an Apply (scoring) operation. Oracle Data Miner automatically selects output for query. The default output is Feature Set.

- Add Feature Function
You can manually add and define feature functions in the Add Feature Function dialog box.

- Edit Feature Function
You can edit a feature function in the edit Edit Feature Function dialog box.

Related Topics

- Add Additional Output

- Add Partitioning Columns

- Modify Input

Edit Feature Prediction Outputs

The output of a Predictive Query is the output of an Apply (scoring) operation. Oracle Data Miner automatically selects output for query. The default output is Feature Set.

You can also select feature ID, feature details, and feature value. You can edit the parameters of the functions and perform the following tasks:

- Delete: To delete an output, select the output and click .

- Add: To add an output, click . Use the Add Feature Function dialog box to select an output.

- Edit: To edit an output, either double-click the function or select the function and click . Use the Edit Feature Function dialog box to make changes.

Related Topics

- Apply Functions

- Add Feature Function

- Edit Feature Function

Add Feature Function

You can manually add and define feature functions in the Add Feature Function dialog box.

To add a feature function:

1. In the Function field, select a function from the drop-down list. The options are:

- Feature ID

- Feature Value

- Feature Details

- Feature Set

2. To specify a default name instead of using the default name, deselect Auto. This turns off automatic selection.

3. Click OK.

Edit Feature Function

You can edit a feature function in the edit Edit Feature Function dialog box.

To edit a feature function:

1. Select the change that you want to make to the function.

2. To specify a name instead of using the default name, deselect Auto. This turns off automatic selection.

3. Click OK.

Feature Extraction Query Properties

In the Properties pane, you can examine and change the characteristics or properties of a node.

To view the properties of a node, click the node and click Properties. If the Properties pane is closed, then go to View and click Properties. Alternately, right-click the node and click Go to Properties.

The Feature Extraction Query Properties has these sections:

- Feature Predictions: Displays the predictions produced by the query.

- Partition

- Additional Output: Displays the output specified.

- Cache

- Details

Feature Extraction Query Context Menu

The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

To view the Feature Extraction Query node context menu, right-click the node. The following options are available in the context menu:

- Connect
Use the Connect option to link nodes in a workflow.

- Run
Use the Run option to execute the tasks specified in the nodes that comprise the workflow.

- Force Run
Use the Force Run option to rerun one or more nodes that are complete.

- Create Schedule
Use the Create Schedule option to define a schedule to run a workflow at a definite time and date.

- Edit
Use the Edit option to edit the default settings of a node.

- View Data
Use the View Data option to view the data contained in a Data node.

- Generate Apply Chain
Use the Generate Apply Chain to create a new node that contains the specification of a node that performs a transformation.

- Show Event Log
Use the Show Event Log option to view information about events in the current connection, errors, warnings, and information messages.

- Deploy
Use the Deploy option to deploy a node or workflow by creating SQL scripts that perform the tasks specified in the workflow.

- Cut
Use the Cut option to remove the selected object, which could be a node or connection.

- Copy
Use the Copy option to copy one or more nodes and paste them into the same workflow or a different workflow.

- Paste
Use the Paste option to paste the copied object in the workflow.

- Extended Paste
Use the Extended Paste option to preserve node and model names while pasting them.

- Select All
Use the Select All option to select all the nodes in a workflow.

- Performance Settings
Use the Performance Settings option to edit Parallel settings and In-Memory settings of the nodes.

- Toolbar Actions
Use the Toolbar Action option to select actions in the toolbar from the context menu.

- Show Runtime Errors
Use the Show Runtime Errors to view errors related to node failure during runtime. This option is displayed only when running of the node fails at runtime.

- Show Validation Errors
Use the Show Validation Errors option to view validation errors, if any.

- Save SQL
Use the Save SQL option to generate SQL script for the selected node.

- Validate Parents
Use the Validate Parents option to validate all parent nodes of the current node.

- Go to Properties
Use the Go to Properties option to open the Properties pane of the selected node.

Related Topics

- Edit Feature Extraction Query

- View Data for a Predictive Query

Connect

Use the Connect option to link nodes in a workflow.

To connect nodes:

1. Right-click a node and click Connect. Alternately, go to Diagram and click Connect.

2. Use the cursor to draw a line from this node to the target node.

3. Click to establish the connection. Note the following:

- You can create only valid connections.

- You can create only one connection between any two nodes.

- You can remove a connection by pressing the ESC key.

Related Topics

- Connect

Run

Use the Run option to execute the tasks specified in the nodes that comprise the workflow.

The Data Miner server runs workflows asynchronously. The client does not have to be connected. You can run one or more nodes in the workflow:

- To run one node: Right-click the node and select Run.

- To run multiple nodes simultaneously: Select the nodes by holding down the Ctrl key and click each individual node. Then right-click any selected node and select Run.

If a node depends on outputs of one or more parent nodes, then the parent node runs automatically only if the outputs required by the running node are missing.

Force Run

Use the Force Run option to rerun one or more nodes that are complete.

Force Run deletes any existing models before building them once again.

To select more than one node, click the nodes while holding down the Ctrl key.

You can Force Run a node at any location in a workflow. Depending on the location of the node in the workflow, you have the following choices for running the node using Force Run:

- Selected Node

- Selected Node and Children (available if the node has child nodes)

- Child Node Only (available if the node one or more child nodes)

- Selected Node and Parents (available if the node has parent nodes)

Create Schedule

Use the Create Schedule option to define a schedule to run a workflow at a definite time and date.

In the Create Schedule dialog box, you can create schedules for your workflows. To create workflow schedule:

1. Start Date: Select a date to set as the start date of the schedule. Click to select a date.

2. Repeat: Select any one of the following options:

- None: To schedule the workflow to run only once at the defined time.

- Every Day: To schedule the workflow to run daily at the specified time.

- Every Week: To schedule the workflow to run weekly at the specified time.

- Custom: To customize your workflow schedule, click Custom. This opens the Repeat dialog box, where you can set how frequently the workflow should run.

3. End Repeat: You can select any one of the following options:

- None: To continue running the workflow every hour.

- After: Select a number by clicking the arrows. This runs the workflow every hour, and would stop after the number of hours you have selected here. For example, if you select 8, then the workflow will run every hour, and after 8 hours, it will stop.

- On Date: Select a particular date by clicking the calendar icon.

4. Select Use Existing Schedule, and select a schedule from the drop-down list if you want to schedule the workflow as per the selected schedule.

- Click to edit the selected schedule in the Schedule dialog box.

- Click to add a new schedule. You can also edit the selected schedule, and add it here.

- Click to delete the selected schedule.

5. Click OK.

To save the workflow schedule settings, click . You can provide a name for the schedule in the Save a Schedule dialog box.

Related Topics

- Repeat

- Save a Schedule

Edit

Use the Edit option to edit the default settings of a node.

Nodes have default algorithms and settings. When you edit a node, the default algorithms and settings are modified. You can edit a node in any one of the following ways:

- Edit nodes using the Edit dialog box

- Edit nodes through Properties UI

Related Topics

- Editing Nodes through Edit Dialog Box

- Editing Nodes through Properties

View Data

Use the View Data option to view the data contained in a Data node.

The Data nodes are Create Table or View node, Data Source node, Explore Data node, Graph node, SQL Query node, and Update Table node.

Related Topics

- Data Source Node Viewer

Generate Apply Chain

Use the Generate Apply Chain to create a new node that contains the specification of a node that performs a transformation.

If you have several transformations performed in sequence, for example, Sample followed by a Custom transform, then you must select Generate Apply Chain for each transformation in the sequence.You must connect the individual nodes and connect them to an appropriate data source.

Generate Apply Chain helps you create a sequence of transformations that you can use to ensure that new data is prepared in the same way as existing data. For example, to ensure that Apply data is prepared in the same way as Build data, use this option.

The Generate Apply Chain option is not valid for all nodes. For example, it does not copy the specification of a Build node.

Show Event Log

Use the Show Event Log option to view information about events in the current connection, errors, warnings, and information messages.

Clicking the Show Event Log option opens the View and Event Log dialog box.

Related Topics

- View an Event Log

Deploy

Use the Deploy option to deploy a node or workflow by creating SQL scripts that perform the tasks specified in the workflow.

The scripts generated by Deploy are saved to a directory.

Note:

You must run a node before deploying it.

You can generate a script that replicates the behavior of the entire workflow. Such a script can serve as the basis for application integration or as a light-weight deployment than the alternative of installing the Data Miner repository and workflows in the target and production system.

To deploy a workflow or part of a workflow:

1. Right-click a node and select Deploy.

2. Select any one of the deployment options:

- Selected node and dependent nodes

- Selected node, dependent nodes, and child nodes

- Selected node and connected nodes

3. After selecting the deployment option, the Generate SQL Script wizard opens. In the wizard, enter details for the following:

Related Topics

- Deploy Workflows using Object Generation Scripts

- Running Workflow Scripts

Cut

Use the Cut option to remove the selected object, which could be a node or connection.

You can also delete objects by selecting them and pressing DELETE on your keyboard.

Copy

Use the Copy option to copy one or more nodes and paste them into the same workflow or a different workflow.

To copy and paste nodes:

1. Select the nodes to copy. To select several nodes, hold down the Ctrl key when you click the nodes.

The selected node is highlighted. In this example Classification is selected. The other node is not selected.

2. Right-click and select Copy from the context menu. Alternately, you can press Ctrl+C to copy the selected nodes.

Note:

Copying and pasting nodes do not carry any mining models or results from the original node.

Paste

Use the Paste option to paste the copied object in the workflow.

To paste an object, right-click the workflow and click Paste. Alternately, you can press Ctrl+V.

Note:

Node names and model names are changed to avoid naming collisions. To preserve names, use the option Extended Paste.

Related Topics

- Copy

- Extended Paste

Extended Paste

Use the Extended Paste option to preserve node and model names while pasting them.

The default behavior of Paste is to change node names and model names to avoid naming collisions.

To go to the Extended Paste option, right-click the workflow and click Extended Paste. Alternately, you can press Control+Shift+V.

Note:

If model names are not unique, then the models may be overwritten when they are rebuilt.

Related Topics

- Copy

Select All

Use the Select All option to select all the nodes in a workflow.

The selected nodes and links are highlighted in a dark blue border.

Performance Settings

Use the Performance Settings option to edit Parallel settings and In-Memory settings of the nodes.

If you click Performance Settings in the context menu, or if you click Performance Options in the workflow toolbar, then the Edit Selected Node Settings dialog box opens. It lists all the nodes that comprise the workflow. To edit the settings in the Edit Selected Node Settings dialog box:

- Click Parallel Settings and select:

- Enable: To enable parallel settings in the selected nodes in the workflow.

- Disable: To disable parallel settings in the selected nodes in the workflow.

- All: To turn on parallel processing for all nodes in the workflow.

- None: To turn off parallel processing for all nodes in the workflow.

- Click In-Memory Settings and select:

- Enable: To enable In-Memory settings for the selected nodes in the workflow.

- Disable: To disable In-Memory settings for the selected nodes in the workflow.

- All: To turn on In-Memory settings for the selected nodes in the workflow.

- None: To turn off In-Memory settings for all nodes in the workflow

- Click to set the Degree of Parallel, and In-Memory settings such as Compression Method, and Priority Levels in the Edit Node Performance Settings dialog box.
If you specify parallel settings for at least one node, then this indication appears in the workflow title bar:

Performance Settings is either On for Selected nodes, On (for All nodes), or Off. You can click Performance Options to open the Edit Selected Node Settings dialog box.

- Click to edit default the preferences for parallel processing.

- Edit Node Default Settings: You can edit the Parallel Settings and In-Memory settings for the selected node in the Performance Options dialog box. You can access the Performance Options dialog box from the Preferences options in the SQL Developer Tools menu.

- Change Settings to Default

Related Topics

- About Parallel Processing

- Edit Node Performance Settings

- Performance Options

Toolbar Actions

Use the Toolbar Action option to select actions in the toolbar from the context menu.

Current actions are Zoom In and Zoom Out.

Related Topics

- Managing Workflows using Workflow Controls

Show Runtime Errors

Use the Show Runtime Errors to view errors related to node failure during runtime. This option is displayed only when running of the node fails at runtime.

The Event Log opens with a list of errors. Select the error to see the exact message and details.

Related Topics

- View an Event Log

Show Validation Errors

Use the Show Validation Errors option to view validation errors, if any.

This option is displayed only when there are validation errors. For example, if an Association node is not connected to a Data Source node, then select Show Validation Errors to view the validation error No build data input node connected.

You can also view validation errors by moving the mouse over the node. The errors are displayed in a tool tip.

Save SQL

Use the Save SQL option to generate SQL script for the selected node.

To generate SQL script for the selected node:

1. Right-click the node and click Save SQL.

2. Select any one of the options to save the generated SQL script:

- SQL to Clipboard

- SQL to File

- SQL Script to Clipboard

- SQL Script to File

When you save to a file, the system provides a default location. You can browse to change this location. You can also create a folder for scripts.

The saved SQL includes SQL generated by the current node and all of its parent nodes that are data providers. The SQL lineage ends when it encounters a node that represents persisted objects, such as tables or models.

The generated script does not generate all behavior of the node. The script does not create any objects. For example, if you select Save SQLfor a Create Table node, then it does not generate a script to create the table. Instead, it generates a script to query the created table.

Related Topics

- Deploy Workflows using Data Query Scripts

Validate Parents

Use the Validate Parents option to validate all parent nodes of the current node.

To validate parent nodes of a node, right-click the node and select Validate Parents.

You can validate parent nodes when the node is in Ready, Complete and Error state. All parent nodes must be in completed state.

Go to Properties

Use the Go to Properties option to open the Properties pane of the selected node.

Related Topics

- Managing Workflows and Nodes in the Properties Pane

Prediction Query

A Prediction Query node performs classification and regression using the input.

The data type of the target determines whether classification or regression is performed. A Prediction Query can run in parallel.

Note:

Predictive Query nodes require Oracle 12c Release 1 or later.

This section on Prediction Query node contains the following topics:

- Create a Prediction Query
You create a Prediction Query node to perform the data mining functions Classification or Regression on the input data, depending on the type of input data.

- Edit a Prediction Query
In the Edit Prediction Query Node dialog box, you can specify or change the characteristics of the models to build.

- Run Predictive Query Node
You must run a Predictive Query node to view the data.

- View Data for a Predictive Query
For Predictive Query Nodes, the View Data viewer displays the output from a node is run. It also displays the results when the query is applied to a small subset of the data.

- Prediction Query Properties
In the Properties pane, you can examine and change the characteristics or properties of a node.

- Prediction Query Node Context Menu
The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

Related Topics

- About Parallel Processing

- Classification

- Regression

Create a Prediction Query

You create a Prediction Query node to perform the data mining functions Classification or Regression on the input data, depending on the type of input data.

To create a Prediction Query in an existing workflow:

1. In the Components pane, go to Workflow Editor. If the Components pane is not visible, then in the SQL Developer menu bar, go to View and click Components. Alternately, press Ctrl+Shift+P to dock the Components pane.

2. Create a Data Source node containing the input data.

3. Expand the Predictive Queries section in the Components pane.

4. Drag and drop the node from the Components pane to the Workflow pane.
The node is added to the workflow. The GUI shows that the node has no data associated with it. Therefore, it cannot be run.

5. Connect the Data Source Node to the Prediction Query Node.

6. Edit the Prediction Query node.

7. Run the Predictive Query node and view the date.

8. To save the results of the query, use a Create Table or View node.

Related Topics

- Edit a Prediction Query

- Run Predictive Query Node

- View Data for a Predictive Query

Edit a Prediction Query

In the Edit Prediction Query Node dialog box, you can specify or change the characteristics of the models to build.

To edit a Prediction Query node:

1. Double-click the Prediction Query node or right-click the node and click Edit. The Edit Prediction Query Node dialog box opens.

2. In the Edit Prediction Query Node dialog box, enter the details in the following tabs:

- Predictions tab:

- In the Case ID field, select a case ID from the drop-down list. The case ID is optional. It is recommended that you specify a case ID to uniquely define each record. The case ID helps with model repeatability and is consistent with good data mining practices.

- In the Targets section, you can add one or more targets. To add target, click . Use the Add Target dialog box to define targets.

- In the Targets section, change the Mining Type of a target, if necessary. Each attribute has an associated data type and mining type. The mining type defines how the attribute is treated in the predictive query. For Regression query analysis, the mining type must be Numerical. For Classification query analysis, the mining type must be Categorical. If you make changes, then click OK.

- In the Prediction Output section, you can edit the prediction output. Oracle Data Miner automatically determines the output for each target. You can modify the output.

- Partition tab: You can perform the following tasks:

- Add one or more Partition attributes. This is optional. Selecting a Partition attribute directs the predictive query to build a virtual model for each unique partition. To add partitions, click . Use the Add Partitioning Columns dialog box to select partitions.

- Specify partitioning expressions.

- Input tab: You can perform the following tasks:

- Modify input

- Add or remove inputs

- Change mining types of inputs

- Additional Output tab: You can add outputs (optional). By default, all target columns, the Case ID column, and partitioning columns are automatically added to Additional Output. To make changes, click Additional Output.

3. Click OK.

- Add Target
You must add at least one target for prediction. Targets can have different mining types.

- Add Partitioning Columns
Partitioning columns result in building a virtual model for each unique partition. Because the virtual model uses data only from a specific partition, it can potentially predict cases more accurately than if you did not select a partition.

- Edit Prediction Output
The output of a Predictive Query is the output of an Apply (Scoring) operation. Oracle Data Miner automatically selects output for the target. The default output is listed in the Predictions Output section in the Predictions tab.

- Modify Input
The Input tab shows all columns that are used as input for the Predictive Query.

- Add Additional Output
The Output tab shows the columns that will be used in the output to identify the prediction data.

Add Target

You must add at least one target for prediction. Targets can have different mining types.

To add a target:

1. Select one or more attributes in the Available Attributes list to serve as prediction targets. The targets do not have to have the same data type.

You can select nested attributes as targets if they are of type ODMR_NETSTED_*. For example, you could use a join to create a nested attribute consisting of all products purchased by a customer; this attribute would be nested and have the data type ODMR_NESTED_VARCHAR2.

2. Move the target columns to the Selected Attributes list using the arrows.

3. Click OK. The selected attributes are added to the Targets list.

Add Partitioning Columns

Partitioning columns result in building a virtual model for each unique partition. Because the virtual model uses data only from a specific partition, it can potentially predict cases more accurately than if you did not select a partition.

In addition to selecting attributes, you can specify partitioning expressions. Partitioning expressions are concatenated and the result expression is the same for all predictive functions.

1. Select one or more attributes in the Available Attributes list to serve as partitions.

2. Move the selected columns to the Selected Attributes list using the arrows.

3. Click OK. The attributes are moved to the Partition list.

Optionally, you can add partitioning expressions.

- Add Partitioning Expressions
Use Expression Builder to create an expression.

Add Partitioning Expressions

Use Expression Builder to create an expression.

To specify a partitioning expression, click .

Suppose one of the partitions is AGE. Here is a sample partitioning expression:

CASE WHEN AGE < 20 THEN 1
WHEN AGE >=20 AND AGE < 40 THEN 2
WHEN AGE >=40 AND AGE < 60 THEN 3
ELSE 4
END

Suppose this expression is named Expression_1. After you run the node, the output includes a column titled Expression_1. This column will contain the value 1 if AGE is less than 20, 2 if AGE is 20 or greater but less than 40, and so on.

Related Topics

- Expression Builder

Edit Prediction Output

The output of a Predictive Query is the output of an Apply (Scoring) operation. Oracle Data Miner automatically selects output for the target. The default output is listed in the Predictions Output section in the Predictions tab.

- For Classification, the default output are:

- Prediction

- Prediction Details

- Prediction Probability

You can also select Prediction Set.

- For Regression, the default outputs are:

- Prediction

- Prediction Details

For Classification or Regression, you can edit the parameters of functions and the output for each target one at a time. You can perform the following tasks:

- To edit Prediction Output Function, select a target and click . You edit output for each target one at a time. Use the Edit Prediction Function dialog box to make changes.

- To delete an output, select the output and click .

- To add an output, select the target in the Targets section and click . Use the Add Prediction Output Function dialog box to select an output.

- Add Prediction Output Function

- Edit Prediction Function Dialog

Related Topics

- Apply Functions

Add Prediction Output Function

To add Prediction Output Function:

1. In the Function field, select a function from the drop-down list.

2. To specify a default name instead of using the default name, deselect Auto. This turns off automatic selection.

3. Click OK.

Edit Prediction Function Dialog

To edit Prediction Function:

1. Select the change that you want to make to the function.

2. To specify a name instead of using the default name, deselect Auto. This turns off automatic selection.

3. Click OK.

Modify Input

The Input tab shows all columns that are used as input for the Predictive Query.

Target (for Prediction Query) and Case ID columns are identified with special icons. The Rule column in the grid explains why an attribute is not used.

By default, Determine inputs automatically (using heuristics) is selected. After you run the node, you can click the link to View Heuristic Results Details. To change the inputs, deselect Determine inputs automatically (using heuristics). You can perform the following tasks:

- Override defaults, and add or remove input columns.

- Change Mining Types: To change the mining type of a column, click the Mining Type entry for the column, and select a new mining type from the drop-down list.

- Ignore columns: If you do not want to use a column as input, then click the input entry for the attribute and select from the drop-down list. It ignored the selected column, and not used for input. To use a column, select from the drop-down list.

- Search column: To search for columns, use the Find field.

- View Heuristic Results Details
The View Heuristic Results Details dialog box provides detailed information about automatic changes made to the input.

View Heuristic Results Details

The View Heuristic Results Details dialog box provides detailed information about automatic changes made to the input.

For example, the mining type is changed to Categorical when the number of unique values is less than the threshold value of 5. A column that has a constant value is excluded (not used as input).

Add Additional Output

The Output tab shows the columns that will be used in the output to identify the prediction data.

By default, all target columns for Prediction Query, the Case ID column, and partitioning columns are automatically added to Additional Output. You can perform the following tasks:

- Add Additional Output: To add additional output, click Automatic to turn off automatic selection. Then click . Use the Add Supplemental dialog to add columns to the output.

- Remove Output Columns: To remove columns, select the column and click .

- Add Supplemental Dialog
In the Add Supplemental dialog box, you can include or exclude columns to be used in the output for a Prediction Query.

Add Supplemental Dialog

In the Add Supplemental dialog box, you can include or exclude columns to be used in the output for a Prediction Query.

Select the columns and use the arrows to move them from the Available Attributes list to the Selected Attributes list.

Run Predictive Query Node

You must run a Predictive Query node to view the data.

To run Predictive Query nodes, right-click the node and select either Run or View Data. Virtual models may take a while to be formulated. The View Data option generates a small sample output of the query.

Regardless of how you run it, select View Data to view the results of the query.

Related Topics

- View Data for a Predictive Query

View Data for a Predictive Query

For Predictive Query Nodes, the View Data viewer displays the output from a node is run. It also displays the results when the query is applied to a small subset of the data.

The Predictive Query node must be run successfully.

To view data for a Predictive Query node:

1. Right-click the node and select View Data.

2. You can either sort or filter the data.

3. Click OK.

You can view prediction details to see Prediction Details in a separate window.

- View Prediction Details

Related Topics

- Data Source Node Viewer

View Prediction Details

To view the prediction details:

1. Click the details that you want to view.

2. Then, click . The details are displayed in the View Value dialog box. You can also search for specific values.

Prediction Query Properties

In the Properties pane, you can examine and change the characteristics or properties of a node.

To view the properties of a node, click the node and click Properties. If the Properties pane is closed, then go to View and click Properties. Alternately, right-click the node and click Go to Properties.

The Prediction Query Node Properties has these sections:

- Predictions: Displays the predictions produced by the query.

- Partition

- Additional Output: Displays the output specified.

- Cache

- Details

Prediction Query Node Context Menu

The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

To view the Prediction Query node context menu, right-click the node. The following options are available in the context menu:

- Connect
Use the Connect option to link nodes in a workflow.

- Run
Use the Run option to execute the tasks specified in the nodes that comprise the workflow.

- Force Run
Use the Force Run option to rerun one or more nodes that are complete.

- Create Schedule
Use the Create Schedule option to define a schedule to run a workflow at a definite time and date.

- Edit
Use the Edit option to edit the default settings of a node.

- View Data
Use the View Data option to view the data contained in a Data node.

- Generate Apply Chain
Use the Generate Apply Chain to create a new node that contains the specification of a node that performs a transformation.

- Show Event Log
Use the Show Event Log option to view information about events in the current connection, errors, warnings, and information messages.

- Deploy
Use the Deploy option to deploy a node or workflow by creating SQL scripts that perform the tasks specified in the workflow.

- Cut
Use the Cut option to remove the selected object, which could be a node or connection.

- Copy
Use the Copy option to copy one or more nodes and paste them into the same workflow or a different workflow.

- Paste
Use the Paste option to paste the copied object in the workflow.

- Extended Paste
Use the Extended Paste option to preserve node and model names while pasting them.

- Select All
Use the Select All option to select all the nodes in a workflow.

- Performance Settings
Use the Performance Settings option to edit Parallel settings and In-Memory settings of the nodes.

- Toolbar Actions
Use the Toolbar Action option to select actions in the toolbar from the context menu.

- Show Runtime Errors
Use the Show Runtime Errors to view errors related to node failure during runtime. This option is displayed only when running of the node fails at runtime.

- Show Validation Errors
Use the Show Validation Errors option to view validation errors, if any.

- Save SQL
Use the Save SQL option to generate SQL script for the selected node.

- Validate Parents
Use the Validate Parents option to validate all parent nodes of the current node.

- Go to Properties
Use the Go to Properties option to open the Properties pane of the selected node.

Related Topics

- Edit a Prediction Query

- View Data for a Predictive Query

Connect

Use the Connect option to link nodes in a workflow.

To connect nodes:

1. Right-click a node and click Connect. Alternately, go to Diagram and click Connect.

2. Use the cursor to draw a line from this node to the target node.

3. Click to establish the connection. Note the following:

- You can create only valid connections.

- You can create only one connection between any two nodes.

- You can remove a connection by pressing the ESC key.

Related Topics

- Connect

Run

Use the Run option to execute the tasks specified in the nodes that comprise the workflow.

The Data Miner server runs workflows asynchronously. The client does not have to be connected. You can run one or more nodes in the workflow:

- To run one node: Right-click the node and select Run.

- To run multiple nodes simultaneously: Select the nodes by holding down the Ctrl key and click each individual node. Then right-click any selected node and select Run.

If a node depends on outputs of one or more parent nodes, then the parent node runs automatically only if the outputs required by the running node are missing.

Force Run

Use the Force Run option to rerun one or more nodes that are complete.

Force Run deletes any existing models before building them once again.

To select more than one node, click the nodes while holding down the Ctrl key.

You can Force Run a node at any location in a workflow. Depending on the location of the node in the workflow, you have the following choices for running the node using Force Run:

- Selected Node

- Selected Node and Children (available if the node has child nodes)

- Child Node Only (available if the node one or more child nodes)

- Selected Node and Parents (available if the node has parent nodes)

Create Schedule

Use the Create Schedule option to define a schedule to run a workflow at a definite time and date.

In the Create Schedule dialog box, you can create schedules for your workflows. To create workflow schedule:

1. Start Date: Select a date to set as the start date of the schedule. Click to select a date.

2. Repeat: Select any one of the following options:

- None: To schedule the workflow to run only once at the defined time.

- Every Day: To schedule the workflow to run daily at the specified time.

- Every Week: To schedule the workflow to run weekly at the specified time.

- Custom: To customize your workflow schedule, click Custom. This opens the Repeat dialog box, where you can set how frequently the workflow should run.

3. End Repeat: You can select any one of the following options:

- None: To continue running the workflow every hour.

- After: Select a number by clicking the arrows. This runs the workflow every hour, and would stop after the number of hours you have selected here. For example, if you select 8, then the workflow will run every hour, and after 8 hours, it will stop.

- On Date: Select a particular date by clicking the calendar icon.

4. Select Use Existing Schedule, and select a schedule from the drop-down list if you want to schedule the workflow as per the selected schedule.

- Click to edit the selected schedule in the Schedule dialog box.

- Click to add a new schedule. You can also edit the selected schedule, and add it here.

- Click to delete the selected schedule.

5. Click OK.

To save the workflow schedule settings, click . You can provide a name for the schedule in the Save a Schedule dialog box.

Related Topics

- Repeat

- Save a Schedule

Edit

Use the Edit option to edit the default settings of a node.

Nodes have default algorithms and settings. When you edit a node, the default algorithms and settings are modified. You can edit a node in any one of the following ways:

- Edit nodes using the Edit dialog box

- Edit nodes through Properties UI

Related Topics

- Editing Nodes through Edit Dialog Box

- Editing Nodes through Properties

View Data

Use the View Data option to view the data contained in a Data node.

The Data nodes are Create Table or View node, Data Source node, Explore Data node, Graph node, SQL Query node, and Update Table node.

Related Topics

- Data Source Node Viewer

Generate Apply Chain

Use the Generate Apply Chain to create a new node that contains the specification of a node that performs a transformation.

If you have several transformations performed in sequence, for example, Sample followed by a Custom transform, then you must select Generate Apply Chain for each transformation in the sequence.You must connect the individual nodes and connect them to an appropriate data source.

Generate Apply Chain helps you create a sequence of transformations that you can use to ensure that new data is prepared in the same way as existing data. For example, to ensure that Apply data is prepared in the same way as Build data, use this option.

The Generate Apply Chain option is not valid for all nodes. For example, it does not copy the specification of a Build node.

Show Event Log

Use the Show Event Log option to view information about events in the current connection, errors, warnings, and information messages.

Clicking the Show Event Log option opens the View and Event Log dialog box.

Related Topics

- View an Event Log

Deploy

Use the Deploy option to deploy a node or workflow by creating SQL scripts that perform the tasks specified in the workflow.

The scripts generated by Deploy are saved to a directory.

Note:

You must run a node before deploying it.

You can generate a script that replicates the behavior of the entire workflow. Such a script can serve as the basis for application integration or as a light-weight deployment than the alternative of installing the Data Miner repository and workflows in the target and production system.

To deploy a workflow or part of a workflow:

1. Right-click a node and select Deploy.

2. Select any one of the deployment options:

- Selected node and dependent nodes

- Selected node, dependent nodes, and child nodes

- Selected node and connected nodes

3. After selecting the deployment option, the Generate SQL Script wizard opens. In the wizard, enter details for the following:

Related Topics

- Deploy Workflows using Object Generation Scripts

- Running Workflow Scripts

Cut

Use the Cut option to remove the selected object, which could be a node or connection.

You can also delete objects by selecting them and pressing DELETE on your keyboard.

Copy

Use the Copy option to copy one or more nodes and paste them into the same workflow or a different workflow.

To copy and paste nodes:

1. Select the nodes to copy. To select several nodes, hold down the Ctrl key when you click the nodes.

The selected node is highlighted. In this example Classification is selected. The other node is not selected.

2. Right-click and select Copy from the context menu. Alternately, you can press Ctrl+C to copy the selected nodes.

Note:

Copying and pasting nodes do not carry any mining models or results from the original node.

Paste

Use the Paste option to paste the copied object in the workflow.

To paste an object, right-click the workflow and click Paste. Alternately, you can press Ctrl+V.

Note:

Node names and model names are changed to avoid naming collisions. To preserve names, use the option Extended Paste.

Related Topics

- Copy

- Extended Paste

Extended Paste

Use the Extended Paste option to preserve node and model names while pasting them.

The default behavior of Paste is to change node names and model names to avoid naming collisions.

To go to the Extended Paste option, right-click the workflow and click Extended Paste. Alternately, you can press Control+Shift+V.

Note:

If model names are not unique, then the models may be overwritten when they are rebuilt.

Related Topics

- Copy

Select All

Use the Select All option to select all the nodes in a workflow.

The selected nodes and links are highlighted in a dark blue border.

Performance Settings

Use the Performance Settings option to edit Parallel settings and In-Memory settings of the nodes.

If you click Performance Settings in the context menu, or if you click Performance Options in the workflow toolbar, then the Edit Selected Node Settings dialog box opens. It lists all the nodes that comprise the workflow. To edit the settings in the Edit Selected Node Settings dialog box:

- Click Parallel Settings and select:

- Enable: To enable parallel settings in the selected nodes in the workflow.

- Disable: To disable parallel settings in the selected nodes in the workflow.

- All: To turn on parallel processing for all nodes in the workflow.

- None: To turn off parallel processing for all nodes in the workflow.

- Click In-Memory Settings and select:

- Enable: To enable In-Memory settings for the selected nodes in the workflow.

- Disable: To disable In-Memory settings for the selected nodes in the workflow.

- All: To turn on In-Memory settings for the selected nodes in the workflow.

- None: To turn off In-Memory settings for all nodes in the workflow

- Click to set the Degree of Parallel, and In-Memory settings such as Compression Method, and Priority Levels in the Edit Node Performance Settings dialog box.
If you specify parallel settings for at least one node, then this indication appears in the workflow title bar:

Performance Settings is either On for Selected nodes, On (for All nodes), or Off. You can click Performance Options to open the Edit Selected Node Settings dialog box.

- Click to edit default the preferences for parallel processing.

- Edit Node Default Settings: You can edit the Parallel Settings and In-Memory settings for the selected node in the Performance Options dialog box. You can access the Performance Options dialog box from the Preferences options in the SQL Developer Tools menu.

- Change Settings to Default

Related Topics

- About Parallel Processing

- Edit Node Performance Settings

- Performance Options

Toolbar Actions

Use the Toolbar Action option to select actions in the toolbar from the context menu.

Current actions are Zoom In and Zoom Out.

Related Topics

- Managing Workflows using Workflow Controls

Show Runtime Errors

Use the Show Runtime Errors to view errors related to node failure during runtime. This option is displayed only when running of the node fails at runtime.

The Event Log opens with a list of errors. Select the error to see the exact message and details.

Related Topics

- View an Event Log

Show Validation Errors

Use the Show Validation Errors option to view validation errors, if any.

This option is displayed only when there are validation errors. For example, if an Association node is not connected to a Data Source node, then select Show Validation Errors to view the validation error No build data input node connected.

You can also view validation errors by moving the mouse over the node. The errors are displayed in a tool tip.

Save SQL

Use the Save SQL option to generate SQL script for the selected node.

To generate SQL script for the selected node:

1. Right-click the node and click Save SQL.

2. Select any one of the options to save the generated SQL script:

- SQL to Clipboard

- SQL to File

- SQL Script to Clipboard

- SQL Script to File

When you save to a file, the system provides a default location. You can browse to change this location. You can also create a folder for scripts.

The saved SQL includes SQL generated by the current node and all of its parent nodes that are data providers. The SQL lineage ends when it encounters a node that represents persisted objects, such as tables or models.

The generated script does not generate all behavior of the node. The script does not create any objects. For example, if you select Save SQLfor a Create Table node, then it does not generate a script to create the table. Instead, it generates a script to query the created table.

Related Topics

- Deploy Workflows using Data Query Scripts

Validate Parents

Use the Validate Parents option to validate all parent nodes of the current node.

To validate parent nodes of a node, right-click the node and select Validate Parents.

You can validate parent nodes when the node is in Ready, Complete and Error state. All parent nodes must be in completed state.

Go to Properties

Use the Go to Properties option to open the Properties pane of the selected node.

Related Topics

- Managing Workflows and Nodes in the Properties Pane

11 Text Nodes

Text nodes are available in the Text section of the Components pane. Oracle Text Knowledge Base is required for text processing.

To install Oracle Text Knowledge Base, you must install the Oracle Database Examples. For directions on how to install the examples, see Oracle Database Examples Installation Guide

If you are connected to Oracle Database12c or later, then you can use Automatic Data Preparation (ADP) to prepare text data using the Text tab to specify data usage.

Oracle Data Miner supports the following Text nodes:

- Oracle Text Concepts
Oracle text concepts include the terms Theme, Stopword, Stoplist, and Stoptheme.

- Text Mining in Oracle Data Mining
Text must undergo a transformation process before it can be mined.

- Apply Text Node
The Apply Text node enables you to apply existing text transformations from either a Build Text node or from a Text Node to new data.

- Build Text
Build Text node prepares a data source that has one or more Text columns.

- Text Reference
A Text Reference node enables you to reference text transformations defined in a Build Text node in the current workflow or in a different workflow.

Related Topics

- Oracle Database Examples Installation Guide

Oracle Text Concepts

Oracle text concepts include the terms Theme, Stopword, Stoplist, and Stoptheme.

- Theme: A theme is a topic associated with a given document. A document can have many themes. A theme does not have to appear in a document. For example, a document containing the words San Francisco may have California as one of its themes.

- Stopword: A stopword is a word that is not indexed during text transformations. A stopword is usually a low information word. In English a, the, this, or with are usually stopwords.

- Stoplist: A stoplist is a list of stopwords. Oracle Text supplies a stoplist for every language. By default during indexing, the system uses the Oracle Text default stoplist for your language. You can edit the default stoplist or create a new one.

Note:

In Oracle Data Miner, stoplists are shared across all transformations and are not owned by a specific transformation.

- Stoptheme: A stoptheme is a theme to be skipped over during indexing. Stopthemes are specified by adding them to stoplists.

Oracle Text uses stopwords and stopthemes to indicate text that can be safely ignored during text mining.

The Oracle Text Lexer breaks source text into tokens or themes—usually words—in accordance with a specified language. To extract tokens, the Lexer uses parameters as defined by a lexer preference. These parameters include:

- Definitions for the characters that separate tokens. For example, whitespace.

- Conditions to convert text to all uppercase or not.

- Text analysis text to create theme tokens. This is done when theme indexing is enabled.

Text Mining in Oracle Data Mining

Text must undergo a transformation process before it can be mined.

After the data has been properly transformed, a case table can be used for building, testing, or scoring data mining models. The case table must be a relational table. It cannot be created as a view.

A Source table for Oracle Data Mining can include one or more columns of text. A text column cannot be used as a target.

The following Oracle Data Mining algorithms support text:

- Anomaly Detection (one-class Support Vector Machine)

- Classification algorithms: Naive Bayes, Generalized Linear Models, and Support Vector Machine
Decision Tree when you connect to Oracle Database 12c or later

- Clustering algorithms: k- Means and Expectation Maximization

- Feature Extraction algorithms: Nonnegative Matrix Factorization, Singular Value Decomposition, and Principal Components Analysis

- Regression algorithms: Generalized Linear Models and Support Vector Machine

Note:

These algorithms do not support text:

- O-Cluster

- Decision Tree when you connect to Oracle Database 11g

- Association (Apriori)

Any text attributes are automatically filtered out for model builds when you use O-Cluster or Decision Tree connected to Oracle Database 11g.

- Data Preparation for Text
Data Preparation for text depends on which version of Oracle Database that you connect to.

Data Preparation for Text

Data Preparation for text depends on which version of Oracle Database that you connect to.

- Text Processing in Oracle Data Mining 12c Release 1 (12.1) and Later
In Oracle Data Mining 12c Release 1 (12.1) and later, if unstructured text data is present, then text processing includes text transformation before text mining.

- Text Processing in Oracle Data Mining 11g Release 2 (11.2) and Earlier
In Oracle Data Mining 11g Release 2 (11.2) and earlier, before text mining is done, it must undergo Feature Extraction and Text preparation processes.

Text Processing in Oracle Data Mining 12c Release 1 (12.1) and Later

In Oracle Data Mining 12c Release 1 (12.1) and later, if unstructured text data is present, then text processing includes text transformation before text mining.

Oracle Data Mining includes significant enhancements in text processing that simplify the data mining process (model build, deployment, and scoring) when unstructured text data is present in the input. Some points about unstructured text and text transformation:

- Unstructured text includes data items such as web pages, document libraries, Microsoft Power Point presentations, product specifications, email messages, comment fields in reports, and call center notes.

- CLOB columns and long VARCHAR2 columns are automatically interpreted as unstructured text by Oracle Data Mining.

- Columns of short VARCHAR2, CHAR, BLOB, and BFILE can be specified as unstructured text.

- To transform unstructured text for mining, Oracle Data Mining uses Oracle Text utilities and term weighting strategies.

- Text terms are extracted and given numeric values in a text index.

- Text transformation process is configurable for models and individual attributes. You can specify data preparation for text nodes when you define a model node.

After text transformation, the text can be mined with a data mining algorithm.

Note:

If you connect to Oracle 12c Release 1 or later, then it is not always necessary to use the Text nodes, Apply Text node, Build text, and Text Reference.

Related Topics

- Apply Text Node

- Build Text

- Text Reference

- Text

Text Processing in Oracle Data Mining 11g Release 2 (11.2) and Earlier

In Oracle Data Mining 11g Release 2 (11.2) and earlier, before text mining is done, it must undergo Feature Extraction and Text preparation processes.

The following processes are:

- Extraction or Feature Extraction: This is a special preprocessing step, where the text is broken down into units (terms) that can be mined. Text terms can be keywords or other document-derived features.

- Text preparation: Text preparation uses a Build Text node to transform text columns. Build Text does not support HTML or XML documents. It also does not support any binary data types.

Oracle Data Miner uses the facilities of Oracle Text to preprocess text columns.

Note:

You must preprocess text using the Text nodes, Apply Text node, Build Text, and Text Reference.

Apply Text Node

The Apply Text node enables you to apply existing text transformations from either a Build Text node or from a Text Node to new data.

This ensure that the apply data is transformed in the same way that the build data was transformed.

Apply Text can run in parallel.

- Default Behavior for the Apply Text Node
Apply Text node applies existing text transformations from either a Build Text or Text Node to new data.

- Create an Apply Text Node
You create an Apply Text node to apply existing text transformations from either a Build Text node or from a Text Node to new data.

- Edit Apply Text Node
The Edit Apply Text Node dialog box enables you to view the text transformations performed on the Build data.

- Apply Text Node Properties
In the Properties pane, you can examine and change the characteristics or properties of a node.

- Apply Text Node Context Menu
The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

Default Behavior for the Apply Text Node

Apply Text node applies existing text transformations from either a Build Text or Text Node to new data.

This ensures that the apply data is transformed in the same way that the build data was transformed.

Note:

All models in the node must have the same case ID.

Create an Apply Text Node

You create an Apply Text node to apply existing text transformations from either a Build Text node or from a Text Node to new data.

Before creating the Apply Text node, first create a workflow. Then create a Data Source node.

To create an Apply Text node:

1. In the Components pane, go to Workflow Editor. If the Components pane is not visible, then in the SQL Developer menu bar, go to View and click Components. Alternately, press Ctrl+Shift+P to dock the Components pane.

2. In the Workflow Editor, expand Text and click Apply Text.

3. Drag and drop the node from the Components pane to the Workflow pane.
The node is added to the workflow. The GUI shows that the node has no data associated with it. Therefore, it cannot be run.

4. Move to the node that provides data for Apply Text. Right-click and select Connect. Drag the line to the Build Text node and click again.

Note:

The Apply data must be compatible with the Build data.

5. Move to the Build Text node or Text node that indicates how the text columns were prepared. For example, go to the Build Text node for the model to be applied. Link the Build Text node to the Apply node.

6. To view or modify the transformation details, right-click the Apply Text node and select Edit. This opens the Edit Apply Text Node dialog box.

7. The node is ready to run. Right-click the node and select Run.

Related Topics

- Edit Apply Text Node

Edit Apply Text Node

The Edit Apply Text Node dialog box enables you to view the text transformations performed on the Build data.

The Apply data must be prepared in the same way as the Build data.

To open the Edit Apply Text Node dialog box right-click the Apply Text node and select Editor just double-click the node.

1. Right-click the Apply Text node and click Edit. Alternately, you can double-click the Apply Text node.

2. The Edit Apply Text Node has two panes:

- In the top pane, you can perform the following tasks:

- Case ID: Specify a case ID in this field. This is optional.

- View Attributes: Select All or Text and Transformed from the drop-down list. For each attribute, the following are displayed: Type: The data type of the attribute. The type of an attribute that has a text transform applied is DM_NESTED_NUMERICALS. Source: The source column for a transformed column. Transform: The type of text transform—Token or Theme. Output: Indicates if the attribute is passed on to subsequent nodes. By default, all nodes are passed on.

- View Stoplist. To view the stoplist, select a transformed column and click View Stoplist. The Stoplist Editor starts. You can view the items in the stoplist.

- View and Edit text transform: To view the definition of a text transformation, select a transformed attribute and click . This opens the Add/Edit Text Transform dialog box.

- Exclude attributes: To exclude attributes, select the option and in the Output column of the grid, click . The icon changes to . The excluded attribute is not passed on to subsequent operations. You may want to exclude the non-transformed version of a text column.

- Include attributes: To include an attribute, click the icon again. The icon changes to , indicating that it is included.

- In the lower pane, you can view the text transformation after running the node.

3. Click OK.

- View the Text Transform (Apply)
You can view the effects of the text transformation defined in the Build Text node or the Text node in the View Text Transform window.

Related Topics

- Add/Edit Text Transform

- Stoplist Editor

- View the Text Transform (Apply)

View the Text Transform (Apply)

You can view the effects of the text transformation defined in the Build Text node or the Text node in the View Text Transform window.

To access the View Text Transform window:

1. Open the Edit Apply Text Node dialog box by double-clicking the Apply Text node or right-clicking the node and select Edit.

2. In the Upper pane, select the name of the transformed attribute.

3. In the lower pane, you can perform the following tasks:

- Click Tokens or Themes. A grid displays all of the tokens or themes in the document and the frequency of each token or theme. Use the search field to search for topics or themes by name, the default, or by frequency.

- Click the Output to view the tokens or themes for a sample of the attributes. Output Sample lists a sample of the attributes listed by case ID, if you specified one, or by row ID if you did not specify a case ID. You can search the IDs. Click an ID. The original text from the untransformed attribute is displayed along with the tokens or themes identified in it, along with the frequency of each token or theme.

4. Click OK.

Apply Text Node Properties

In the Properties pane, you can examine and change the characteristics or properties of a node.

To view the properties of a node, click the node and click Properties. If the Properties pane is closed, then go to View and click Properties. Alternately, right-click the node and click Go to Properties.

The Apply Text Properties has the following sections:

- Transforms
In the Transforms tab, you can view and edit transformations defined in the Edit Build Text Node dialog box.

- Cache
The Cache section provides the option to generate a cache for output data. You can change this default using the transform preference.

- Sample
The data is sampled to support data analysis.

- Details
The Details section displays the node name and comments about the node.

Related Topics

- Properties

Transforms

In the Transforms tab, you can view and edit transformations defined in the Edit Build Text Node dialog box.

Related Topics

- Edit Build Text Node

Cache

The Cache section provides the option to generate a cache for output data. You can change this default using the transform preference.

You can perform the following tasks:

- Generate Cache of Output Data to Optimize Viewing of Results: Select this option to generate a cache. The default setting is to not generate a cache.

- Sampling Size: You can select caching or override the default settings. Default sampling size is Number of Rows Default Value=2000

Related Topics

- Transforms

Sample

The data is sampled to support data analysis.

The default is to use a sample. The Sample tab has the following selections:

- Use All Data: By default, Use All Data is deselected.

- Sampling Size: The default is Number of Rows with a value of 2000. You can change sampling size to Percent. The default is 60 percent.

Details

The Details section displays the node name and comments about the node.

You can change the name of the node and edit the comments from this tab. The new node name and comments must satisfy the node name and node comments requirements.

Related Topics

- Node Name and Node Comments

Apply Text Node Context Menu

The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

To view the Apply Text node context menu, right-click the node. The following options are available in the context menu:

- Connect

- Edit. Opens Edit Apply Text Node dialog box.

- Validate Parents

- Run

- Force Run

- View Data

- Deploy

- Show Graph

- Generate Apply Chain

- Cut

- Copy

- Paste

- Extended Paste

- Select All

- Performance Settings. This opens the Edit Selected Node Settings dialog box, where you can set Parallel Settings and In-Memory settings for the node.

- Show Event Log

- Show Runtime Errors. Displayed if there are errors.

- Show Validation Errors. Displayed if there are validation errors.

- Navigate

Related Topics

- Edit Apply Text Node

Build Text

Build Text node prepares a data source that has one or more Text columns.

You can use the data to build models.

Build Text can run in parallel.

- Default Behavior of the Build Text Node
The Build Text node enables you to define a text transformation for each test column.

- Create Build Text Node
You create a Build Text node to prepare a data source that has one or more Text columns.

- Edit Build Text Node
The Edit Build Text Node dialog box enables you to define transformations for text columns. The transformed text columns can be used in data mining.

- Build Text Node Properties
In the Properties pane, you can examine and change the characteristics or properties of a node.

- Build Text Node Context Menu
The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

Related Topics

- About Parallel Processing

Default Behavior of the Build Text Node

The Build Text node enables you to define a text transformation for each test column.

You can use the transformed columns to build models using any algorithm that supports text.

Note:

O-Cluster and Decision Tree do not support text.

A Build Text node builds one model using the NMF algorithm by default. The transformed column or columns are passed to subsequent nodes and the non-transformed columns are not passed on.

All models in the node have the same case ID.

Create Build Text Node

You create a Build Text node to prepare a data source that has one or more Text columns.

Before creating a Build Text node, create a workflow. Then identify or create a Data Source node.

To create a Build Text node:

1. In the Components pane, go to Workflow Editor. If the Components pane is not visible, then in the SQL Developer menu bar, go to View and click Components. Alternately, press Ctrl+Shift+P to dock the Components pane.

2. In the Workflow Editor, expand Text and click Build Text.

3. Drag and drop the node from the Components pane to the Workflow pane.
The node is added to the workflow. The GUI shows that the node has no data associated with it. Therefore, it cannot be run.

4. Move to the node that provides data for the Build Text node. Right-click and select Connect. Drag the line to the Build Text node and click again.

5. Either accept the default settings or edit the text details. To edit the transformation details, right-click the node and select Edit. The Edit Build Text Node dialog box opens.

6. The node is ready to run. Right-click the node and select Run.

Related Topics

- Edit Build Text Node

Edit Build Text Node

The Edit Build Text Node dialog box enables you to define transformations for text columns. The transformed text columns can be used in data mining.

To open the Edit Build Text Node dialog box:

1. Right-click the Build Text node and select Edit. Alternately, double-click the node. The Edit Build Text Node dialog box opens.

2. The Edit Build Text Node dialog box has two panes.

- In the top pane, you can perform the following tasks:

- Specify the case ID (optional).

- Open the Stoplist Editor.

- View attributes: Select All or Text and Transformed from the drop-down list. For each attribute, the following are displayed: Type: The data type of the attribute. The type of an attribute that has a text transform applied is DM_NESTED_NUMERICALS. Source: The source column for a transformed column. Transform: The type of text transform—Token or Theme. Output: Indicates if the attribute is passed on to subsequent nodes. By default, all nodes are passed on.

- Define transformation: To define a transformation, select a text attribute and click . Define the text transformation in the Add/Edit Text Transform dialog box. Repeat the step for each text attribute.

- Edit Transformation: Select the transformed attribute and click .

- Delete Transformation: Select the transformed attribute and click .

- Exclude attribute: To exclude attributes, select it and in the Output column of the grid, click . The icon changes to . The excluded attribute is not passed on to subsequent operations. You may want to exclude the non-transformed version of a text column.

- Include attribute: To include an attribute, click the icon again. The icon changes to , indicating that it is included.

- In the lower pane, you can view the test transformation after the node is run.

3. Click OK.

- View the Text Transform
In the View Text Transform dialog box, you can view the output sample of the tokens or themes for a sample of attributes.

- Add/Edit Text Transform
You can add and edit text related transformation settings in the Add/Edit Text Transform dialog box.

- Stoplist Editor
In the Stoplist Editor, you can either edit an existing stoplist, or you can create a new stoplist. Stoplists are shared among all workflows.

View the Text Transform

In the View Text Transform dialog box, you can view the output sample of the tokens or themes for a sample of attributes.

To view the effects of a text transformation:

1. Open the Edit Build Text Node dialog box by double-clicking the Build Text node or right-clicking the node and select Edit.

2. In the upper pane, select the name of the transformed attribute.

3. In the lower pane:

- Click Tokens or Themes. A grid displays all the tokens or themes in the document and the frequency of each token or theme. Use the search field to search for topics or themes by name, the default, or by frequency.

- To add a token or theme to the stoplist, select it and click Add to Stoplist.

- Click Output to view the tokens or themes for a sample of the attributes. Output Sample lists a sample of the attributes listed by case ID, if you specified one, or by row ID if you did not specify a case ID. You can search the IDs.

- Click an ID. The original text from the non-transformed attribute is displayed along with the tokens or themes identified in it, along with the frequency of each token or theme.

4. Click OK.

Add/Edit Text Transform

You can add and edit text related transformation settings in the Add/Edit Text Transform dialog box.

The Add/Edit Text Transform dialog box can be opened from the Edit Build Text Node dialog box. To open or edit a text transform, click The default values for the transformation are illustrated in this graphic:

- Source Column: This is the name of the column to be transformed.

- Transform Type: This is either Token (the default) or Theme.

- Output Column: This is the name of the new column. The default name is the source column name with either TOK (for Token) or THM (for Theme) appended, depending on the transformation type. To specify the output column name, deselect Automatic and enter a name in the Output Column field.

In the Settings section, specify characteristics of the text and the transform:

- Language: Select any one of the following options:

- Single Language: By default, a single language is specified. English is the default language. You can select a different language.

- Multiple Language: Select this option to specify multiple language. For example, to specify Single Byte languages, such as Arabic, Turkish, Thai, and European languages, select them from the Single Byte list. To specify Multibyte languages, such as Chinese (simplified or traditional), Japanese or Korean, select them from the Multibyte languages.

- Stoplist: Oracle Text provides default stoplists for several single languages. If there is a default stoplist, then it is selected. For several languages, the default is no stoplist. You can select any stoplist that was previously created for this attribute from the drop-down list. You can perform the following tasks:

- Edit a Stoplist: To edit a stoplist, click . The Stoplist Editor opens.

- Add a Stoplist: To add a stoplist, click . The Stoplist Editor opens.

- Token: If you select Token, then the defaults are:

- Maximum number per document: 50 (default)

- Maximum number across all document: 3000 (default)

You can change these values. The tokens per document and across all documents cutoffs are for rankings, not for an absolute count of tokens. You could have more than 3000 tokens across all documents if there were ties.

- Theme: If you select theme, then the defaults are:

- Maximum number per documents: 50 (default)

- Maximum number across all document: 3000 (default)

You can change these values. The themes per document and across all documents cutoffs are for rankings, not for absolute count of themes. You could have more then 3000 themes across all documents if there were ties.

Theme incudes a Theme Type specification. The default is Single. You can select Full.

- Frequency: The default is Term Frequency. You can select Term Frequency IDF.

Note:

Frequency is a sticky setting. If you change it, then the changed value becomes the default.

Term Frequency uses the term frequency in the document itself. It does not take collection information into account.

Term Frequency IDF is the traditional TF-IDF. It takes into account information from the document (Term Frequency) and collection-level information (IDF plus the terms to use if a maximum overall number of terms for the collection is set).

TF-IDF (Term Frequency–Inverse Document Frequency) is a weight often used in information retrieval and text mining. This weight is a statistical measure used to evaluate how important a word is to a document in a collection. The importance increases proportionally to the number of times a word appears in the document, but is offset by the frequency of the word in the collection.

Related Topics

- Stoplist Editor

Stoplist Editor

In the Stoplist Editor, you can either edit an existing stoplist, or you can create a new stoplist. Stoplists are shared among all workflows.

You can edit any stoplist in this dialog box, not just the ones associated with transformations defined in this node.

To access the stoplist editor, open the Edit Build Text Node by double-clicking a Build Text node. To view, edit, and create a stoplist:

1. Click Edit Stoplist.

2. The Stoplist Editor opens. All stoplists for all transformations are listed.

3. To add a stoplist, click . The New Stoplist Editor wizard opens.

4. To modify an existing stoplist, select the stoplist from the Custom Stoplists list.

The items in the stoplist are listed in the bottom pane.

- To delete an item from the stoplist, select the item and click .

- To add stopwords or stopthemes to the selected list, click . The Add Stopwords/Stopthemes dialog box opens.

5. To delete a stoplist, select it in the Custom Stoplists list and click .

- New Stoplist Editor
By using the New Stoplist Editor wizard, you can create new stoplists, edit stoplists, and combine stoplists.

- Add Stopwords/Stopthemes
In the Add Stopwords/Stopthemes dialog box, you can add stopwords and stopthemes to a stoplist.

New Stoplist Editor

By using the New Stoplist Editor wizard, you can create new stoplists, edit stoplists, and combine stoplists.

You can perform the following tasks:

- Create new stoplists. To create a stoplist, click . The New Stoplist Wizard starts. The wizard has two steps:

- Stoplist Definition

- Review

- Remove words from an existing stoplist.

- Combine several stoplists to create a new one. For example, if the document is in both French and English, then you can combine the French and English stoplists.

- Create an empty stoplist to which you must add all stopwords and stopthemes.

- Stoplist Definition

- Review

Stoplist Definition

Follow these steps to define a stoplist:

1. Either accept the provided name or enter a different name.

2. The default selection Extends following stoplist(s) enables you to create a new stoplist by combining and modifying existing stoplists.

Select one or more stoplists to extend. If you select several stoplists, then they are combined.

3. To create a completely new stoplist, select Empty. and select a language for the stoplist. The default is English.

4. Click Next.

Review

To add or remove stopwords and stopthemes:

1. To add items to the stoplist, click . The Add Stopwords/Stopthemes dialog box opens.

2. To remove items from the stoplist, select it and click .

3. Click Finish.

Related Topics

- Add Stopwords/Stopthemes

Add Stopwords/Stopthemes

In the Add Stopwords/Stopthemes dialog box, you can add stopwords and stopthemes to a stoplist.

To add Stopwords or Stopthemes:

1. Enter the stopwords, separated by commas.

2. Click OK when you have finished.

Build Text Node Properties

In the Properties pane, you can examine and change the characteristics or properties of a node.

To view the properties of a node, click the node and click Properties. If the Properties pane is closed, then go to View and click Properties. Alternately, right-click the node and click Go to Properties.

The Build Text node Properties pane has the following sections:

- Transforms
In the Transforms tab, you can view and edit transformations defined in the Edit Build Text Node dialog box.

- Sample
The data is sampled to support data analysis.

- Cache
The Cache section provides the option to generate a cache for output data. You can change this default using the transform preference.

- Details
The Details section displays the node name and comments about the node.

Transforms

In the Transforms tab, you can view and edit transformations defined in the Edit Build Text Node dialog box.

Related Topics

- Edit Build Text Node

Sample

The data is sampled to support data analysis.

The default is to use a sample. The Sample tab has the following selections:

- Use All Data: By default, Use All Data is deselected.

- Sampling Size: The default is Number of Rows with a value of 2000. You can change sampling size to Percent. The default is 60 percent.

Cache

The Cache section provides the option to generate a cache for output data. You can change this default using the transform preference.

You can perform the following tasks:

- Generate Cache of Output Data to Optimize Viewing of Results: Select this option to generate a cache. The default setting is to not generate a cache.

- Sampling Size: You can select caching or override the default settings. Default sampling size is Number of Rows Default Value=2000

Related Topics

- Transforms

Details

The Details section displays the node name and comments about the node.

You can change the name of the node and edit the comments in this section. The new node name and comments must meet the requirements.

Related Topics

- Node Name and Node Comments

Build Text Node Context Menu

The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

Right-click a Build Text node. The following options are available in the context menu:

- Connect

- Edit. Edits the text apply. Opens the Edit Build Text Node dialog box.

- Validate Parents

- Run

- Force Run

- View Data

- Deploy

- Show Graph

- Generate Apply Chain

- Cut

- Copy

- Paste

- Extended Paste

- Select All

- Performance Settings. This opens the Edit Selected Node Settings dialog box, where you can set Parallel Settings and In-Memory settings for the node.

- Show Event Log

- Show Runtime Errors. Displayed, if there are errors.

- Show Validation Errors. Displayed, if there are validation errors.

- Navigate

Related Topics

- Edit Build Text Node

Text Reference

A Text Reference node enables you to reference text transformations defined in a Build Text node in the current workflow or in a different workflow.

For example, if you have one workflow that builds a Text model (that is, a workflow that includes a Build Text node) and you want to create a separate workflow that applies the model created in the first workflow, then you can use a Text Reference to provide the text transformation information required by Apply Text.

- Create a Text Reference Node
You create a Text Reference node to reference text transformations that are defined in a Build Text node in the current workflow or in a different workflow.

- Edit Text Reference Node
The Edit Text Reference Node dialog box enables you to select a Build Text node so that you can use its transformations in the current location in the current workflow.

- Text Reference Node Properties
In the Properties pane, you can examine and change the characteristics or properties of a node.

- Text Reference Node Context Menu
The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

Create a Text Reference Node

You create a Text Reference node to reference text transformations that are defined in a Build Text node in the current workflow or in a different workflow.

Before creating a Text Reference node, create a workflow. Then, identify or create a data source.

To create a Build Text node:

1. In the Components pane, go to Workflow Editor. If the Components pane is not visible, then in the SQL Developer menu bar, go to View and click Components. Alternately, press Ctrl+Shift+P to dock the Components pane.

2. In the Workflow Editor, expand Text and click Text Reference.

3. Drag and drop the node from the Components pane to the Workflow pane.
The node is added to the workflow. The GUI shows that the node has no data associated with it. Therefore, it cannot be run.

4. Go to the Edit Text Reference Node dialog box to select a Build Text node to reference.

5. The node is ready to be used. Connect it to an Apply Text node. The Text Reference node is used instead of a Build Text node.

Related Topics

- Apply Text Node

- Edit Text Reference Node

Edit Text Reference Node

The Edit Text Reference Node dialog box enables you to select a Build Text node so that you can use its transformations in the current location in the current workflow.

To open the Edit Text Reference Node:

1. Right-click the node and select Edit. Alternately, double-click the node. The Edit Text Reference Node dialog box has two panes.

2. In the upper pane, click Select. The Select Text Reference Node dialog box opens.

3. After you select a Build Text node, you can view tokens or themes for any transformed nodes. Select a transformed node in the upper pane.

4. In the bottom pane, the Tokens and Themes and their frequencies are displayed. You can search by token or theme (the default) or by frequency

5. Click OK.

- Select Build Text Node
In the Select Build Text Node dialog box, you can select a Build Text node that is either in the current workflow, (the default) or in all workflows.

Select Build Text Node

In the Select Build Text Node dialog box, you can select a Build Text node that is either in the current workflow, (the default) or in all workflows.

Show specifies the list of Build Text nodes to select from.

1. In the Show field, select either All Workflows or Current Workflows (default).

2. In the Search field, you can search for Build Text nodes by project (the default), workflow, or node.

3. Select a Build Text node from the Available Nodes grid. For each Build Text node the grid shows project, workflow, and status.

4. Click OK.

Note:

You cannot select a Text node that is not complete.

Text Reference Node Properties

In the Properties pane, you can examine and change the characteristics or properties of a node.

To view the properties of a node, click the node and click Properties. If the Properties pane is closed, then go to View and click Properties. Alternately, right-click the node and click Go to Properties.

The Text Reference node Properties pane has the following sections:

- Transforms
The Transforms dialog box for the Text reference node displays the transformation related information selected in the Edit Text Reference Node dialog box.

- Details
The Details section displays the node name and comments about the node.

Transforms

The Transforms dialog box for the Text reference node displays the transformation related information selected in the Edit Text Reference Node dialog box.

You can select a different Build Text node from the Properties pane.

Related Topics

- Edit Text Reference Node

Details

The Details section displays the node name and comments about the node.

You can change the name of the node and edit the comments in this section. The new node name and comments must meet the requirements.

Related Topics

- Node Name and Node Comments

Text Reference Node Context Menu

The context menu options depend on the type of the node. It provides the shortcut to perform various tasks and view information related to the node.

Right-click a Text Reference node. The following selections are displayed:

- Connect

- Edit.

- Validate Parents

- Run

- Force Run

- View Data

- Deploy

- Show Graph

- Generate Apply Chain

- Cut

- Copy

- Paste

- Extended Paste

- Select All

- Performance Settings. This opens the Edit Selected Node Settings dialog box, where you can set Parallel Settings and In-Memory settings for the node.

- Show Event Log

- Show Runtime Errors. Displayed if there are errors.

- Show Validation Errors. Displayed if there are validation errors.

- Navigate

Related Topics

- Edit Text Reference Node

12 Testing and Tuning Models

Testing a model enables you to estimate how accurate the predictions of a model are. You can test Classification models and Regression models, and tune Classification models.

This section contains the following topics:

- Testing Classification Models
Classification models are tested by comparing the predicted values to known target values in a set of test data.

- Tuning Classification Models
When you tune a model, you create a derived cost matrix to use for subsequent Test and Apply operations.

- Testing Regression Models
Regression models are tested by comparing the predicted values to known target values in a set of test data.

Testing Classification Models

Classification models are tested by comparing the predicted values to known target values in a set of test data.

The historical data for a Classification project is typically divided into two data sets:

- One for building the model

- One for testing the model

The test data must be compatible with the data used to build the model and must be prepared in the same way that the build data was prepared.

These are the ways to test Classification and Regression models:

- By splitting the input data into build data and test data. This is the default. The test data is created by randomly splitting the build data into two subsets. 40 percent of the input data is used for test data.

- By using all the build data as test data.

- By attaching two Data Source nodes to the build node.

- The first data source that you connect to the build node is the source of the build data.

- The second node that you connect is the source of the test data.

- By deselecting Perform Test in the Test section of the Properties pane and using a Test node. The Test section define how tests are done. By default, all Classification and Regression models are tested.

Oracle Data Miner provides test metrics for Classification models so that you can evaluate the model.

After testing, you can tune the models.

- Test Metrics for Classification Models
Test metrics assess how accurately the model predicts the known values.

- Compare Classification Test Results
By using the Compare Test Result context menu option in a Test node and Classification node, you can compare test results of a Classification model that are tested in a Test node, and for models that are tested after running the Classification node respectively.

- Classification Model Test Viewer
The Classification Model Test viewer displays all information related to the Classification Model test results.

- Viewing Test Results
You can view results of models that are tested in a Classification node and a Test node.

Related Topics

- Test Node

- Tuning Classification Models

Test Metrics for Classification Models

Test metrics assess how accurately the model predicts the known values.

Test settings specify the metrics to be calculated and control the calculation of the metrics. By default, Oracle Data Miner calculates the following metrics for Classification models:

- Performance
The performance measures that are calculated are Predictive Confidence, Average Accuracy, Overall Accuracy, Cost and Cost.

- Performance Matrix
A Performance Matrix displays the number of correct and incorrect predictions made by the model compared with the actual classifications in the test data.

- Receiver Operating Characteristics (ROC)
Receiver Operating Characteristics (ROC) analysis is a useful method for evaluating Classification models. ROC applies to binary classification only.

- Lift
Lift measures the degree to which the predictions of a Classification model are better than randomly-generated predictions. Lift applies to binary classification and non-binary classifications.

- Profit and ROI
Profit uses user-supplied values for startup cost, incremental revenue, incremental cost, budget, and population to maximize the profit.

Related Topics

- Classification Model Test Viewer

- Viewing Models in Model Viewer

- Data Miner Preferences

Performance

The performance measures that are calculated are Predictive Confidence, Average Accuracy, Overall Accuracy, Cost and Cost.

You can view these values separately, and also view all of them at the same time. To view the performance measures:

1. Select the measure that you want to display. Alternately, click All Measures from the Measures list.

2. Use the Sort By lists to sort the measures. You can sort by:

- Name (default)

- Measures

- Creation date
The sort can be by descending order (default) or ascending order.

- Predictive Confidence
Predictive Confidence provides an estimate of how accurate the model is. Predictive Confidence is a number between 0 and 1.

- Average Accuracy
Average Accuracy refers to the percentage of correct predictions made by the model when compared with the actual classifications in the test data.

- Overall Accuracy
Overall Accuracy refers to the percentage of correct predictions made by the model when compared with the actual classifications in the test data.

- Cost
In a Classification model, it is important to specify the costs involved in making an incorrect decision. By doing so, it can be useful when the costs of different misclassifications vary significantly.

Predictive Confidence

Predictive Confidence provides an estimate of how accurate the model is. Predictive Confidence is a number between 0 and 1.

Oracle Data Miner displays Predictive Confidence as a percentage. For example, the Predictive Confidence of 59 means that the Predictive Confidence is 59 percent (0.59).

Predictive Confidence indicates how much better the predictions made by the tested model are than predictions made by a naive model. The Naive Bayes model always predicts the mean for numerical targets and the mode for categorical targets.

Predictive Confidence is defined by the following formula:

Predictive Confidence = MAX[(1-Error of model/Error of Naive Model),0]X100

Where:

Error of Model is (1 - Average Accuracy/100)

Error of Naive Model is (Number of target classes - 1) / Number of target classes

- If the Predictive Confidence is 0, then it indicates that the predictions of the model are no better than the predictions made by using the naive model.

- If the Predictive Confidence is 1, then it indicates that the predictions are perfect.

- If the Predictive Confidence is 0.5, then it indicates that the model has reduced the error of a naive model by 50 percent.

Average Accuracy

Average Accuracy refers to the percentage of correct predictions made by the model when compared with the actual classifications in the test data.

The formula to calculate the Average Accuracy is:

Average Accuracy = (TP/(TP+FP)+TN/(FN+TN))/Number of classes*100

Where:

- TP is True Positive.

- TN is True Negative.

- FP is False Positive.

- FN is False Negative.

The average per-class accuracy achieved at a specific probability threshold is greater than the accuracy achieved at all other possible thresholds.

Overall Accuracy

Overall Accuracy refers to the percentage of correct predictions made by the model when compared with the actual classifications in the test data.

The formula to calculate the Overall Accuracy is:

Overall Accuracy = (TP+TN)/(TP+FP+FN+TN)*100

Where:

- TP is True Positive.

- TN is True Negative.

- FP is False Positive.

- FN is False Negative.

Cost

In a Classification model, it is important to specify the costs involved in making an incorrect decision. By doing so, it can be useful when the costs of different misclassifications vary significantly.

For example, suppose the problem is to predict whether a user is likely to respond to a promotional mailing. The target has two categories: YES (the customer responds) and NO (the customer does not respond). Suppose a positive response to the promotion generates $500 and that it costs $5 to do the mailing. Then, the scenarios are:

- If the model predicts YES, and the actual value is YES, then the cost of misclassification is $0.

- If the model predicts YES, and the actual value is NO, then the cost of misclassification is $5.

- If the model predicts NO, and the actual value is YES, then the cost of misclassification is $500.

- If the model predicts NO, and the actual value is NO, then the cost of misclassification is $0.

Algorithms for Classification model use cost matrix during scoring to propose the least expensive solution. If you do not specify a cost matrix, then all misclassifications are counted as equally important.

If you are building an SVM model, then you must specify costs using model weights instead of a cost matrix.

Performance Matrix

A Performance Matrix displays the number of correct and incorrect predictions made by the model compared with the actual classifications in the test data.

Performance Matrix is calculated by applying the model to a hold-out sample (the test set, created during the split step in a classification activity) taken from the build data. The values of the target are known. The known values are compared with the values predicted by the model. Performance Matrix does the following:

- Measures the likelihood of the model to predict incorrect and correct values

- Indicates the types of errors that the model is likely to make

The columns are predicted values and the rows are actual values. For example, if you are predicting a target with values 0 and 1, then the number in the upper right cell of the matrix indicates the false-positive predictions, that is, predictions of 1 when the actual value is 0.

Receiver Operating Characteristics (ROC)

Receiver Operating Characteristics (ROC) analysis is a useful method for evaluating Classification models. ROC applies to binary classification only.

ROC is plotted as a curve. The area under the ROC curve measures the discriminating ability of a binary Classification model. The correct value for the ROC threshold depends on the problem that the model is trying to solve.

ROC curves are similar to lift charts in that they provide a means of comparison between individual models and determine thresholds that yield a high proportion of positive results. An ROC curve does the following:

- Provides a means to compare individual models and determine thresholds that yield a high proportion of positive results.

- Provides insight into the decision-making ability of the model. For example, you can determine how likely the model is to accurately predict the negative or the positive class.

- Compares predicted and actual target values in a Classification model.

- How to Use ROC
Receiver Operating Characteristics (ROC) supports what-if analysis.

How to Use ROC

Receiver Operating Characteristics (ROC) supports what-if analysis.

You can use ROC to experiment with modified model settings to observe the effect on the Performance Matrix. For example, assume that a business problem requires that the false-negative value be reduced as much as possible within the confines of the requirement that the number of positive predictions be less than or equal to some fixed number. You might offer an incentive to each customer predicted to be high-value, but you are constrained by a budget with a maximum of 170 incentives. On the other hand, the false negatives represent missed opportunities, so you want to avoid such mistakes.

To view the changes in the Performance Matrix:

1. Click Edit Custom Operating Point at the upper right corner. The Specify Custom Threshold dialog box opens.

2. In the Specify Custom Threshold dialog box, mention the desired settings, and view the changes in the Custom Accuracy field.

As you change the Performance Matrix, you are changing the probability that result in a positive prediction. Typically, the probability assigned to each case is examined and if the probability is 0.5 or higher, then a positive prediction is made. Changing the cost matrix changes the positive prediction threshold to some value other than 0.5, and the changed value is displayed in the first column of the table beneath the graph.

Lift

Lift measures the degree to which the predictions of a Classification model are better than randomly-generated predictions. Lift applies to binary classification and non-binary classifications.

Lift measures how rapidly the model finds the actual positive target values. For example, lift enables you to figure how much of the customer database you must contact to get 50 percent of the customers likely to respond to an offer.

The x-axis of the graph is divided into quantiles. To view exact values, place the cursor over the graph. Below the graph, you can select the quantile of interest using Selected Quantile. The default quantile is quantile 1.

To calculate lift, Oracle Data Mining does the following:

- Applies the model to test data to gather predicted and actual target values. This is the same data used to calculate the Performance Matrix.

- Sorts the predicted results by probability, that is, the confidence in a positive prediction.

- Divides the ranked list into equal parts, quantiles. The default is 100.

- Counts the actual positive values in each quantile.

You can graph the lift as either Cumulative Lift or as Cumulative Positive Cases (default). To change the graph, select the appropriate value from the Display list. You can also select a target value in the Target Value list.

Profit and ROI

Profit uses user-supplied values for startup cost, incremental revenue, incremental cost, budget, and population to maximize the profit.

Oracle Data Miner calculates profit as follows:

Profit = -1 * Startup Cost + (Incremental Revenue * Targets Cumulative - Incremental Cost * (Targets Cumulative + Non Targets Cumulative)) * Population / Total Targets

Profit can be positive or negative, that is, it can be a loss.

To view the profit predicted by this model, select the Target Value that you are interested in. You can change the Selected Population%. The default is 1 percent.

Return on Investment (ROI) is the ratio of money gained or lost (whether realized or unrealized) on an investment relative to the amount of money invested. Oracle Data Mining uses this formula:

ROI = ((profit - cost) / cost) * 100

where profit = Incremental Revenue * Targets Cumulative, cost = Incremental Cost * (Targets Cumulative + Non Targets Cumulative)

- Profit and ROI Example
The Profit and ROI example illustrates how profit and ROI are calculated.

- Profit and ROI Use Case
The Profit and ROI Use Case depicts how to interpret results for profit and ROI calculations.

Profit and ROI Example

The Profit and ROI example illustrates how profit and ROI are calculated.

To calculate profit:

1. Profit is calculated for a quantile. In this example, profit and ROI for quantile 20 is calculated.

2. Find the value of Targets Cumulative for Quantile 20 by looking at the lift chart data. Suppose that this value is 18.

3. Suppose that the value of Non Targets Cumulative for Quantile 20 is 2. Find this value by looking at the lift chart.

4. Calculate Total Targets which is Targets Cumulative at last Quantile plus Non Targets Cumulative at last Quantile. Suppose that this value is 100.

5. These values are all user provided. You must provide values based on the business problem:

- Startup cost = 1000

- Incremental revenue = 10

- Incremental cost = 5

- Budget = 10000

- Population = 2000

6. Calculate profit using this formula

Profit = -1 * Startup Cost + (Incremental Revenue * Targets Cumulative - Incremental Cost * (Targets Cumulative + Non Targets Cumulative)) * Population / Total Targets

Substituting the values in this example results in

Profit = -1 * 1000 + (10 * 18 - 5 * (18 + 2) * 2000 / 100 = 600

To calculate ROI, use the formula

ROI = ((profit - cost) / cost) * 100

profit = Incremental Revenue * Targets Cumulative, cost = Incremental Cost * (Targets Cumulative + Non Targets Cumulative)

Substituting the values in this example results in

ROI = ((180 - 100) / 100) * 100 = 80

Profit and ROI Use Case

The Profit and ROI Use Case depicts how to interpret results for profit and ROI calculations.

Suppose you run a mail order campaign. You will mail each customer a catalog. You want to mail catalogs to those customers who are likely to purchase things from the catalog.

Here is the input data from Profit and ROI example:

- Startup cost = 1000. This is the total cost to start the campaign.

- Incremental revenue = 10. This is estimated revenue that results from a sale or new customer.

- Budget = 10000. This is the total amount of money that you can spend.

- Population = 2000. This is the total number of cases.

Therefore, each quantile contains 20 cases:

total population /number of quantiles = 2000/100 = 20

The cost to promote a sale in each quantile is (Incremental Cost * number of cases per quantile) = $5 * 20 = $100).

The cumulative costs per quantile are as follows:

- Quantile 1 costs $1000 (startup cost) + $100 (cost to promote a sale in Quantile 1) = $1100.

- Quantile 2 costs $1100 (cost of Quantile 1) + $100 (cost in Quantile 2).

- Quantile 3 costs $1200.

If you calculate all of the intermediate values, then the cumulative costs for Quantile 90 is $10,000 and for Quantile 100 is $11,000. The budget is $10,000. If you look at the graph for profit in Oracle Data Miner, then you should see the budget line drawn in the profit chart on the 90th quantile.

In the Profit and ROI example, the calculated profit is $600 and ROI is 80 percent, which means that if you mail catalogs to first 20 quantiles of the population (400), then the campaign will generate a profit of $600 (which has ROI of 80 percent).

If you randomly mail the catalogs to first 20 quantiles of customers, then the profit is

Profit = -1 * Startup Cost
+ (Incremental Revenue * Targets Cumulative - Incremental Cost
* (Targets Cumulative + Non Targets Cumulative))
* Population / Total Targets
Profit = -1 * 1000 + (10 * 10 - 5 * (10 + 10)) * 2000 / 100 = -$1000

In other words, there is no profit.

Related Topics

- Profit and ROI Example

Compare Classification Test Results

By using the Compare Test Result context menu option in a Test node and Classification node, you can compare test results of a Classification model that are tested in a Test node, and for models that are tested after running the Classification node respectively.

To compare test results for all of the models in a Classification Build node:

- If you tested the models when you ran the Classification node: Right-click the Classification node that contains the models and select Compare Test Results.

- If you tested the Classification models in a Test node: Right-click the Test node that tests the models and select Compare Test Results.

The Classification Model Test viewer that compares the test results, opens. The comparison enables you to select the model that best solves a business problem.

The graphs in the Performance tab for different models are in different colors. In the other tabs, the same color is used for the line indicating measures such as lift.

The color associated with each model is displayed in the bottom page of each tab.

- Compare Test Results
The comparison of Test results for a Classification node are displayed under different categories for performance, performance Matrix, ROC, lift, and profit.

Related Topics

- Classification Model Test Viewer

Compare Test Results

The comparison of Test results for a Classification node are displayed under different categories for performance, performance Matrix, ROC, lift, and profit.

Compare Test Results for Classification are displayed in these tabs:

- Performance: Compares performance results in the top pane for the models listed in the bottom panel.
To edit the list of models, click above pane that lists the models. This opens the Edit Test Selection (Classification and Regression) dialog box. By default, test results for all models are compared.

- Performance Matrix: Displays the Performance Matrix for each model. You can display either Compare models (a comparison of the performance matrices) or Details (the Performance Matrix for a selected model).

- ROC: Compares the ROC curves for the models listed in the lower pane.
To see information for a curve, select a model and click.

To edit the list of models, click above pane that lists the models. This opens the Edit Test Selection (Classification and Regression) dialog box.

- Lift: Compares the lift for the models listed in the lower pane. For more information about lift, see Lift.
To edit the list of models, click above pane that lists the models. This opens the Edit Test Selection (Classification and Regression) dialog box.

- Profit: Compares the profit curves for the models listed in the lower pane.
To edit the list of models, click above pane that lists the models. This opens the Edit Test Selection (Classification and Regression) dialog box.

- Edit Test Selection (Classification and Regression)
By default, test results for all successfully built models in the build node are selected.

Related Topics

- Profit and ROI

- Performance

- Performance Matrix

- Receiver Operating Characteristics (ROC)

Edit Test Selection (Classification and Regression)

By default, test results for all successfully built models in the build node are selected.

If you do not want to view test results for a model, then deselect the model. Click OK when you have finished.

Classification Model Test Viewer

The Classification Model Test viewer displays all information related to the Classification Model test results.

Open the test viewer by selecting either View Test Results or Compare Test Results in the context menu for a Classification node or a Test node that tests Classification models. Select the results to view.

In the Classification Model Test viewer, you can select the comparison levels for:

- Models: (Default)

- Partitions:

- If a partition has never been selected, then the Select Partition dialog box opens.

- If a partition has been previously selected, then it will be loaded. Click the Partition name that is displayed in the Search field, to view the details.

- To change the selected partition, click . This opens the Select Partition dialog box.

The Classification model test viewer shows the following tabs:

- Performance
The Performance tab provides an overall summary of the performance of each model generated.

- Performance Matrix
The Performance Matrix displays the number of correct and incorrect predictions made by the model compared with the actual classifications in the test data.

- ROC
Receiver Operating Characteristics (ROC) compares predicted and actual target values in a binary Classification model.

- Lift
The Lift graph shows the lift from the model (or models) and also shows the lift from a naive model (Random) and the ideal lift.

- Profit
The Profit graph displays information related to profit, budget, and threshold for one or more models.

- Model Partitions
The Model Partition tab displays the information of the model partitions on a node. The number of partitions can be very large, a fetch size limit will be added.

Related Topics

- Test Metrics for Classification Models

Performance

The Performance tab provides an overall summary of the performance of each model generated.

It displays test results for several common test metrics:

- All Measures (default). The Measure list enables you to select the measures to display. By default, all measures are displayed. The selected measures are displayed as graphs. If you are comparing test results for two or more models, then different models have graphs in different colors.

- Predictive Confidence

- Average Accuracy

- Overall Accuracy

- Cost, if you specified costs or the system calculated costs

In the Sort By fields. you can specify the sort attribute and sort order. The first list is the sort attribute: measure, creation date, or name (the default). The second list is the sort order: ascending or descending (default).

Below the graphs, the Models table supplements the information presented in the graph. You can minimize the table using the splitter line. The Models table in the lower panel summarizes the data in the histograms:

- Name, the name of the model along with color of the model in the graphs

- Predictive Confidence percent

- Overall Accuracy percent

- Average Accuracy percent

- Cost, if you specified cost (costs are calculated by Oracle Data Miner for decision trees)

- Algorithm (used to build the model)

- Build Rows

- Test Rows

- Creation date

By default, results for the selected model are displayed. To change the list of models, click and deselect any models for which you do not want to see results. If you deselect a model, then both the histogram and the summary information are removed.

To view the model, select

Related Topics

- Predictive Confidence

- Average Accuracy

- Overall Accuracy

- Cost

Performance Matrix

The Performance Matrix displays the number of correct and incorrect predictions made by the model compared with the actual classifications in the test data.

You can either view the detail for a selected model, or you can compare performance matrices for all models.

- Click Show Details to view test results for one model.

- Click Compare Nodes to compare test results.

- Show Detail
The Show Detail view displays all information related to the selected model.

- Compare Models
Compare Models compares performance information for all models in the node that were tested.

Show Detail

The Show Detail view displays all information related to the selected model.

First, select a model. If you are viewing test results for one model, then the details for that model are displayed automatically.

- In the top pane, Average Accuracy and Overall Accuracy are displayed with a grid that displays the correct predictions for each target value. Cost information is displayed if you have specified costs.

- In the bottom pane, a Performance Matrix with rows showing actual values and columns showing predicted values is displayed for the selected model. The percentage correct and cost are displayed for each column.

Select Show totals and cost to see the total, the percentage correct, and cost for correct and incorrect predictions.

Click to filter your search based on a target.

Compare Models

Compare Models compares performance information for all models in the node that were tested.

- The top pane lists the following for each model:

- Percentage of correct predictions

- Count of correct predictions

- Total case count

- Cost information

To see more detail, select a model and click .

- The bottom pane displays the target value details for the model selected in the top pane. Select the measure. To filter your search by target value, click

- Correct Predictions (default): Displays correct predictions for each value of the target attribute

- Costs: Displays costs for each value of the target

ROC

Receiver Operating Characteristics (ROC) compares predicted and actual target values in a binary Classification model.

To edit and view an ROC:

1. Select the Target Value. The ROC curves for that value are displayed.

2. Click Edit Custom Operating Point to change the operating point. The ROC graph displays a line showing ROC for each model. Points are marked on the graph indicating the values shown in the key at the bottom of the graph. Below the graph, the ROC Summary results table supplements the information presented in the graph. You can minimize the table using the splitter line.

- The Models grid, in the lower pane, contains the following summary information:

- Name

- Area under the curve

- Maximum Overall Accuracy Percentage

- Maximum Average Accuracy Percentage

- Custom Accuracy Percentage

- Model Accuracy Percentage

- Algorithm

- Build Rows

- Test Rows

- Creation Date and Time

- Select a model and click to see the ROC Details dialog box, which displays the statistics for probability thresholds.

- To change the list of models, click to open the Edit Test Result Selection dialog box. By default, results for all models in the node are displayed.

- Edit Test Result Selection
In the Edit test Result Selection dialog box, you can select specific models that you want to compare.

- ROC Detail Dialog
The ROC Detail Dialog displays statistics for probability thresholds.

Related Topics

- How to Use ROC

Edit Test Result Selection

In the Edit test Result Selection dialog box, you can select specific models that you want to compare.

By default, all models are selected in the Edit test Result Selection dialog box. Deselect the check box for those models for which you do not want to see results. If you deselect a model, then both the ROC curve and the details for that model are not displayed.

Click OK when you have finished.

ROC Detail Dialog

The ROC Detail Dialog displays statistics for probability thresholds.

For each probability threshold, the following are displayed:

- True Positive

- False Negative

- False Positive

- True Negative

- True Positive Fraction

- False Positive Fraction

- Overall Accuracy

- Average Accuracy

Click OK to dismiss the dialog box.

Lift

The Lift graph shows the lift from the model (or models) and also shows the lift from a naive model (Random) and the ideal lift.

The x-axis of the graph is divided into quantiles. The lift graph displays at least three lines:

- A line showing the lift for each model

- A red line for the random model

- A vertical blue line for threshold

The Lift viewer compares lift results for a given target value in two or more models. It displays either the Cumulative Positive Cases or the Cumulative Lift.

If you are comparing the lift for two or more models, then the lines for different models are in different colors. The table below the graph shows the name of the model and the color used to display results for that model.

The viewer has the following controls:

- Display: Selects the display option, either Cumulative Positive Cases (default) or Cumulative Lift.

- Target Value: Selects the target value for comparison. The default target value is the least frequently occurring target value.

The threshold is a blue vertical line used to select a quantile. As the threshold moves, the details for each test result in the Lift Detail table changes to the point on the Lift Chart that corresponds to the selected quantile. You move the threshold by dragging the indicator on the quantile line. Here is the quantile set to 20:

Below the graph, a data table supplements the information presented in the graph. You can minimize the table using the splitter line.

The table has the following columns:

- Name, the name of the model along with color of the model in the graph

- Lift Cumulative

- Gain Cumulative Percentage

- Percentage Records Cumulative

- Target Density Cumulative

- Algorithm

- Build Rows

- Test Rows

- Creation Date (date and time)

Above the Models grid is the Lift Detail Dialog icon . Select a model and click the icon to open the Lift Detail dialog box, which displays lift details for 100 quantiles.

To change the list of models, click and deselect any models for which you do not want to see results. If you deselect a model, then both the lift curve and the detail information for that model are not displayed. By default, results for all models in the node are displayed.

- Lift Detail
The Lift Detail dialog box displays statistics for each quantile from 1 to 100.

Related Topics

- Test Metrics for Classification Models

Lift Detail

The Lift Detail dialog box displays statistics for each quantile from 1 to 100.

Threshold probability does not always reflect standard probability. For example, the Classification node enables you to specify three different performance settings:

- Balanced: Apply balance weighting to all target class values.

- Natural: Do not apply any weighting.

- Custom: Apply user- created custom weights file.

The default for Classification models is Balanced. Balanced is implemented by passing weights or costs into the model, depending on the algorithm used.

The threshold probability actually reflects cost rather than standard probability.

To see the difference between Balanced and Natural:

1. Create a Classification model.

2. Select the performance setting options and view the lift details:

- Natural: The threshold probability values are the greatest probabilities for each quantile.

- Balanced: The threshold reflects cost. You see the lowest cost value for each quantile.

Profit

The Profit graph displays information related to profit, budget, and threshold for one or more models.

The Profit graph displays at least three lines:

- A line showing the profit for each model

- A line indicating the budget

- A line indicating the threshold

The threshold is a blue vertical line used to select a quantile. As the threshold moves, the details for each test result in the Lift Detail table changes to the point on the Lift Chart that corresponds to the selected quantile. You can move the threshold by dragging the indicator on the quantile line. Here is the quantile set to 20:

To specify the values for profit, click Profit Settings to open the Profit Setting dialog box.

If you are comparing the profit for two or more models, then the lines for different models are different colors. The table below the graph shows the name of the model and the color used to display results for that model.

The bottom pane contains the Models grid and supplements the information presented in the graph. You can minimize the table using the splitter line.

The table has the following columns:

- Name, the name of the model along with color of the model in the graphs

- Profit

- ROI Percentage

- Records Cumulative Percentage

- Target Density Cumulative

- Maximum Profit

- Maximum Profit Population Percentage

- Algorithm

- Build Rows

- Test Rows

- Creation Date (and time)

Above the Models grid is the Browse Detail icon. Select a model and click to see the Profit Detail dialog box which displays statistics for each quantile from 1 to 100.

To change the list of models, click and deselect any models for which you do not want to see results. If you deselect a model, then both the profit curve and the detail information for that model are not displayed. By default, results for all models in the node are displayed.

- Profit Detail Dialog
The Profit Detail dialog box displays statistics about profit for quantiles 1 to 100.

- Profit Setting Dialog
In the Profit Settings dialog box, you can provide values for profit settings such as budget, increment cost and so on.

Related Topics

- Profit and ROI

- Profit and ROI Example

- Profit and ROI Use Case

- Test Metrics for Classification Models

Profit Detail Dialog

The Profit Detail dialog box displays statistics about profit for quantiles 1 to 100.

Click OK to dismiss the dialog box.

Profit Setting Dialog

In the Profit Settings dialog box, you can provide values for profit settings such as budget, increment cost and so on.

To edit profit settings:

1. Click Profit Settings to change the following values:

- Startup Cost: The cost of starting the process that creates the profit. The default is 1.

- Incremental Revenue: Incremental revenue earned for each correct prediction. The default is 1.

- Incremental Cost: The cost of each additional item. The default is 1.

- Budget: A total cost that cannot be exceeded. The default value is 1.

- Population: The number of individual cases that the model is applied to. The default is 100.

2. Click OK.

Model Partitions

The Model Partition tab displays the information of the model partitions on a node. The number of partitions can be very large, a fetch size limit will be added.

The Model Partition tab displays the following information about the partitioned model:

- Model name

- Partition ID

- Partition Name

- Predictive Confidence

- Overall Accuracy

- Average Accuracy

- Build Rows

- Test Rows

- Cost

- Algorithm type

- Creation Date

You can perform the following tasks:

- Sort data: To sort data, click

- Pin partition: The icon to pin or select a partition is enabled when you select a row. Select a row and click to mark the selected partitioned as pinned in all the Test Result editors. This means that the partition will be loaded when the editor is opened.

- View partition details: Double click the partition name or click to view the details of the partition such as Partition ID, Partition Name, Partition Details Table, and Table Filtering.

- View model details: Click to view the specific partition model details in the Model Viewer.

- Select and view models in the Edit Test Result Selection dialog box: Click to select models and view them in the Edit Test Result Selection dialog box

- Filter model partition: You can filter and sort model partitions based on the model name, partition name, algorithm, and partition keys.

- Select Partition
In the Select Partition dialog box, you can view filtered partitions based on the Partition keys.

Select Partition

In the Select Partition dialog box, you can view filtered partitions based on the Partition keys.

The filtered partitions are displayed in the partition table in the lower panel of the dialog box. To query and view filtered partitions:

1. In the Fetch field, select a number by clicking the arrows.
This limits the number of partitions displayed to the number that you have entered.

2. Select any one of the following options:

- Match all of the following: To consider all the Partition Keys as the matching criteria to fetch the data.

- Match any of the following To consider the selected Partition Key as the matching criteria to fetch the data.

3. Click Query.
This displays the list of filtered partitioned based on the query.

4. Click OK.

Viewing Test Results

You can view results of models that are tested in a Classification node and a Test node.

Test the model in a Classification Node.

You can change the defaults using the preference setting.

1. Run the Classification node.

2. Right-click the node and select View Test Results.

3. To view the model, select the model that you are interested in.
The Classification Model Test Viewer opens.

4. To compare the test results for all models in the node, select Compare Test Results.

Note:
To view the test results of models tested in a Test node, you must test the model in a Test node and run the Test node.

Tuning Classification Models

When you tune a model, you create a derived cost matrix to use for subsequent Test and Apply operations.

Select one of several ways to create a derived cost matrix when tuning a model.

The derived cost matrix is used for any subsequent test and apply operations. Each tuning dialog box has a different goal in how the tuning is performed.

Note:

To tune models, you must test the models in the same node that you build them.

If necessary, you can remove tuning and then re-run the node.

To tune a model:

1. Open the Properties pane for the Build node. Right-click the node and select Go to Properties.

2. Go to the Test section. Select Generate Select Test Results for Model Tuning. The model test operation then generates the unbiased test results (no cost matrix used) for the corresponding test results. The unbiased test results are determined by the Tune Settings dialog box to initialize tuning options. For example, if only ROC is selected for Test Results, then the test operation generates the regular ROC result and the unbiased ROC result.

3. Run the Build node. You must test the models in the Build node. This is the default behavior of the Classification Build node.

4. In Properties pane for the Build node, go to the Models section. Select the models that you want to tune and click the tune icon in the menu bar.

Select Tune from the drop-down list.

5. The Tune Settings dialog box opens with all the available test results.

You can tune a model using one technique. For example, you can tune using costs or using lift, but not using both costs and lift at the same time.

6. If you are tuning more than one model, then select a model from the Models list in the bottom pane of the dialog box. After you tune the first model, return to this pane and select another model.

7. Click the tab for the test result to tune. The tabs are:

- Cost

- Benefit

- ROC

- Lift

- Profit

8. When you finish tuning a model, click Tune in the pane on the right to generate tuning. In the Models list in the bottom pane, the Tune setting changes from Automatic to the new setting.

9. Tune as many models in the node you want. Go to other tuning tabs and perform tuning from there. When you have finished tuning, click OK.

10. All models that have tuning specifications changed during the session have their test results marked as not run. When you run the node again:

- The new cost matrix is generated and inserted into the model.

- A new test result is generated showing full test result information for the behavior of the current model.

11. Run the tuned model. After running of the model is complete, the Models section in Properties indicates how each model was tuned. For example, if you tune a model by changing costs, then the Tune entry for that model is Tune - Cost.

12. Right-click the Build node and select View Test Results for the tuned model to see the effects of the tuning.

You may have to repeat the tuning steps several times to get the desired results. If necessary, you can remove tuning for a model.

- Remove Tuning
You can remove tuning of a Classification by selecting the Automatic option.

- Cost
The Cost tab in Tune Settings enables you to specify costs for target for scoring purposes.

- Benefit
In the Benefit tab, you can specify a benefit for each value of the target. Specifying benefits is useful when there are many target values.

- ROC
ROC is only supported for binary models.

- Lift
Lift measures the degree to which the predictions of a Classification model are better than randomly generated predictions.

- Profit
The Profit tab provides a method for maximizing profit.

Related Topics

- Testing Classification Models

Remove Tuning

You can remove tuning of a Classification by selecting the Automatic option.

To remove tuning for a model:

1. Right-click the node and select Go to Properties.

2. Go to the Models section and click .

3. Select Automatic.

4. Run the node.

Cost

The Cost tab in Tune Settings enables you to specify costs for target for scoring purposes.

By default the cost matrix is initially generated based on all the known target values in the Build Data Source. The cost matrix is set to cost values of 1 to start with.

To specify costs:

1. Open the Properties pane for the Build node. Right-click the node and select Go to Properties.

2. In the Test section, select Generate Select Test Results for Model Tuning and run the node.

3. In the Models section, select the models that you want to tune and click .

4. Select Tune from the drop-down list. The Tune Settings dialog box opens.

5. In the Tune Settings dialog box, go to the Cost tab.

6. If you are tuning more than one model, then select a model from the Models list in the bottom pane. After you tune the first model, return to this pane and select another model.

7. Select the target value for which to specify costs.

8. Select the appropriate option:

- False Positive: Incorrectly identifying a case as a target. (Default)

- False Negative: Incorrectly identifying a case as a non-target.

9. In the Weight field, specify a weight for the cost.

10. Click Apply to add the cost that you just specified to the cost matrix.

11. Define costs for all target values that you are concerned about.

12. To apply the matrix, click Tune in the upper right pane.

13. Click Derived Matrix to view the cost matrix that you created. Examine the derived cost matrix. You can continue tuning by changing any selections that you made.

14. When you have finished, click OK to accept the tuning. Click Cancel to cancel the tuning

To cancel the tuning, click Reset. Tuning returns to Automatic.

To see the impact of the tuning, rerun the model node.

- Costs and Benefits
In a classification problem, you must specify the cost or benefit associated with correct or incorrect classifications.

Related Topics

- Tuning Classification Models

Costs and Benefits

In a classification problem, you must specify the cost or benefit associated with correct or incorrect classifications.

By doing so, it is valuable when the cost of different misclassification varies significantly.

You can create a cost matrix to bias the model to minimize the cost or maximize the benefit. The cost/benefit matrix is taken into consideration when the model is scored.

- Costs

- Benefits

Costs

Suppose the problem is to predict whether a customer is likely to respond to a promotional mail. The target has two categories: YES (the customer responds) and NO (the customer does not respond). Suppose a positive response to the promotion generates $500 and that it costs $5 to do the mailing. After building the model, you compare the model predictions with actual data held aside for testing. At this point, you can evaluate the relative cost of different misclassifications:

- If the model predicts YES and the actual value is YES, then the cost of misclassification is $0.

- If the model predicts YES and the actual value is NO, then the cost of misclassification is $5.

- If the model predicts NO and the actual value is YES, then the cost of misclassification is $495.

- If the model predicts NO and the actual value is NO, then the cost is $0.

Benefits

Using the same costs, you can approach the relative value of the outcomes from a benefits perspective. When you correctly predict a YES (a responder), the benefit is $495. When you correctly predict a NO (a non-responder), the benefit is $5.00 because you can avoid sending out the mailing. Because the goal is to find the lowest cost solution, benefits are represented as negative numbers.

Benefit

In the Benefit tab, you can specify a benefit for each value of the target. Specifying benefits is useful when there are many target values.

The Benefit tab enables you to:

- Specify a benefit for each value of the target. The values specified are applied to the cost benefit matrix.

- Indicate the most important values.

To tune a model using the Benefit tab:

1. Open the Properties pane for the Build node. Right-click the node and select Go to Properties.

2. In the Test section, select Generate Select Test Results for Model Tuning and run the node.

3. In the Models section, select the models that you want to tune and click .

4. Select Tune from the drop-down list. The Tune Settings dialog box opens in a new tab.

5. In the Tune Settings dialog box, click Benefit.

6. If you are tuning more than one model, then select a model from the Models list in the bottom pane. After you tune the first model, return to this pane and select another model.

7. Select the target value for tuning from the Target Value list.

8. Specify benefit values for the target value selected. Benefit values can be positive or negative. If there is more benefit from a target value, then the benefit value should be higher than other benefit values. The default benefit value for each target value is 0.

Enter the benefit value for the selected target in the Benefit box and click Apply to update the Cost Benefits matrix.

9. When you have finished specifying benefit values, click Tune in the right-hand column.

10. Click View to see the derived cost matrix.

11. When you have finished, click OK to accept the tuning, or click Cancel to cancel the tuning.

Related Topics

- Costs and Benefits

- Tuning Classification Models

ROC

ROC is only supported for binary models.

The ROC Tuning tab adds a side panel to the standard ROC Test Viewer. The following information is displayed:

- Performance Matrix in the upper right pane, displays these matrices:

- Overall Accuracy: Cost matrix for the maximum Overall Accuracy point on the ROC chart.

- Average Accuracy: Cost matrix for the maximum Average Accuracy point.

- Custom Accuracy: Cost matrix for the custom operating point.
You must specify a custom operating point for this option to be available.

- Model Accuracy: The current Performance Matrix (approximately) of the current model.
You can use the following calculation to derive Model Accuracy from the ROC result provided:

If there is no embedded cost matrix, then find the 50 percent threshold point or the closest one to it. If there is an embedded cost matrix, then find the lowest cost point. For a model to have an embedded cost matrix, it must have either been tuned or it has a cost matrix or cost benefit defined by the default settings of the Build node.

- The Performance Matrix grid shows the performance matrix for the option selected.

- Click Tune to:

- Select the current performance option as the one to use to tune the model.

- Derive a cost matrix from the ROC result at that probability threshold.

Tune Settings, in the lower part of this panel, is updated to display the new matrix.

- Click Clear to clear any tuning specifications and set tuning to Automatic. In other words, no tuning is performed.

- ROC Tuning Steps
Lists the procedure to perform ROC tuning.

- Receiver Operating Characteristics
Receiver Operating Characteristics (ROC) is a method for experimenting with changes in the probability threshold and observing the resultant effect on the predictive power of the model.

Related Topics

- Select Custom Operating Point

ROC Tuning Steps

Lists the procedure to perform ROC tuning.

To perform ROC tuning:

1. Open the Properties pane for the Build node. Right-click the node and select Go to Properties.

2. In the Test section, select Generate Select Test Results for Model Tuning and run the node.

3. In the Models section, select the models that you want to tune and click .

4. Select Tune from the drop-down list. The Tune Settings dialog box opens in a new tab.

5. In the Tune Settings dialog box, go to the ROC tab.

6. If you are tuning more than one model, then select a model from the Models list in the bottom pane. After you tune the first model, return to this pane and select another model.

7. Select a target value. In the case of ROC, there are only two values.

8. Select a custom operating point if you do no want to use the default point.

9. Select the kind of Performance Matrix to use:

- Overall Accuracy (default)

- Average Accuracy

- Custom Accuracy. Fill in the values for the Performance Matrix if you select this option.

- Model Accuracy

10. Click Tune. New tune settings are displayed in the same panel as the Performance Matrix. Examine the Derived Cost Matrix. You can continue tuning by changing any selections that you made.

11. When you have finished, click OK to accept the tuning, or click Cancel to cancel the tuning.

- To reset the tuning, click Reset.

- To see the impact of the tuning, run the Model node.

- Select Custom Operating Point
The Specify Custom Threshold dialog box allows you to edit the custom operating point for all the models in the node.

Related Topics

- Tuning Classification Models

Select Custom Operating Point

The Specify Custom Threshold dialog box allows you to edit the custom operating point for all the models in the node.

- To change the Hit Rate or False Alarm, click the appropriate option and adjust the value that you want to use.

- Alternatively, you can specify the False Positive or False Negative ratio. To do this, click the appropriate option and specify the ratio.

Click OK when you have finished.

Receiver Operating Characteristics

Receiver Operating Characteristics (ROC) is a method for experimenting with changes in the probability threshold and observing the resultant effect on the predictive power of the model.

- The horizontal axis of an ROC graph measures the False Positive Rate as a percentage.

- The vertical axis shows the True Positive Rate.

- The top left corner is the optimal location in an ROC curve, indicating a high TP (True Positive) rate versus low FP (False Positive) rate.

- The area under the ROC curve measures the discriminating ability of a binary Classification model. This measure is especially useful for data sets with an unbalanced target distribution (one target class dominates the other). The larger the area under the curve, the higher the likelihood that an actual positive case is assigned a higher probability of being positive than an actual negative case.

ROC curves are similar to lift charts in that they provide a means of comparison between individual models, and then determine thresholds that yield a high proportion of positive hits. ROC was originally used in signal detection theory to gauge the true hit versus false alarm ratio when sending signals over a noisy channel.

Lift

Lift measures the degree to which the predictions of a Classification model are better than randomly generated predictions.

To tune a model using Lift:

1. Open the Properties pane for the Build node. Right-click the node and select Go to Properties.

2. In the Test section, select Generate Select Test Results for Model Tuning and run the node.

3. In the Models section, select the models that you want to tune and click .

4. Select Tune from the drop-down list. The Tune Settings dialog box opens in a new tab.

5. In Tune Settings dialog box, go to the Lift tab.

6. If you are tuning more than one model, then select a model from the Models list in the bottom pane. After you tune the first model, return to this pane and select another model.

7. Select the target value for tuning from the Target Value list.

8. Decide whether to tune using the Cumulative Positive Cases chart, the default or the Cumulative Lift Chart. Select the chart from the Display list.

Either chart displays several curves: the lift curve for the model that you are tuning, ideal lift, and random lift, which is the lift from a model where predictions are random.

The chart also displays a blue vertical line that indicates the threshold, the quantile of interest.

9. Selected a quantile using the slider in the quantile display below the lift chart. As you move the slider, the blue vertical bar moves to that quantile, and the tuning panel is updated with the Performance Matrix for that point.

10. Click Tune, below the Performance Matrix. New tune settings are displayed in the same panel as the Performance Matrix. Examine the Derived Cost Matrix. You can continue tuning by changing any selections that you made.

11. When you have finished, click OK to accept the tuning, or click Cancel to cancel the tuning.

- To reset the tuning, click Reset.

- To see the impact of the tuning, run the Model node.

- About Lift
Lift is the ratio of positive responders in a segment to the positive responders in the population as a whole.

Related Topics

- Tuning Classification Models

About Lift

Lift is the ratio of positive responders in a segment to the positive responders in the population as a whole.

For example, if a population has a predicted response rate of 20 percent, but one segment of the population has a predicted response rate of 60 percent, then the lift of that segment is 3 (60 percent/20 percent). Lift measures the following:

- The concentration of positive predictions within segments of the population and specifies the improvement over the rate of positive predictions in the population as a whole.

- The performance of targeting models in marketing applications. The purpose of a targeting model is to identify segments of the population with potentially high concentrations of positive responders to a marketing campaign.

The notion of lift implies a binary target: either a Responder or a Non-responder, which means either YES or NO. Lift can be computed for multiclass targets by designating a preferred positive class and combining all other target class values, effectively turning a multiclass target into a binary target. Lift can be applied to both binary and non-binary classifications as well.

The calculation of lift begins by applying the model to test data in which the target values are already known. Then, the predicted results are sorted in order of probability, from highest to lowest Predictive Confidence. The ranked list is divided into quantiles (equal parts). The default number of quantiles is 100.

Profit

The Profit tab provides a method for maximizing profit.

To tune a model:

1. Open Properties for the Build node. Right-click the node and select Go to Properties.

2. In the Test section, select Generate Select Test Results for Model Tuning and run the node.

3. In the Models section, select the models that you want to tune and click .

4. Select Tune from the drop-down list. The Tune Settings dialog box opens in a new tab.

5. In the Tune Settings dialog box, go to the Profit tab.

6. If you are tuning more than one model, then select a model from the Models list in the bottom pane. After you tune the first model, return to this pane and select another model.

7. Select the target value for tuning from the Target Value list.

8. Click Profit Settings and specify the values in the Profit Settings dialog box.

9. After you specify Profit Settings, the graph reflects the values that you specified.

10. Use the slider below the chart to adjust the Threshold (blue vertical line).

11. Click Tune, below the Performance Matrix. New tune settings are displayed in the same panel as the Performance Matrix. Examine the Derived Cost Matrix. You can continue tuning by changing any selections that you made.

12. When you have finished, click OK to accept the tuning, or click Cancel to cancel the tuning.

- To reset tuning, click Reset.

- To see the impact of the tuning, run the Model node.

- Profit Setting
In the Profit Setting dialog box, you can change default values for profit related settings.

- Profit
Profit provides a method for maximizing profit.

Related Topics

- Tuning Classification Models

Profit Setting

In the Profit Setting dialog box, you can change default values for profit related settings.

The default values for Startup Cost, Incremental Revenue, Incremental Cost, and Budget are all 1. The default value for Population is 100. Change these values to ones appropriate for your business problem.

Click OK.

Profit

Profit provides a method for maximizing profit.

You can specify the information listed below. Oracle Data Miner uses these information to create a cost matrix that optimizes profit:

- Startup cost

- Incremental revenue

- Incremental cost

- Budget

- Population

Related Topics

- Profit Setting Dialog

Testing Regression Models

Regression models are tested by comparing the predicted values to known target values in a set of test data.

The historical data for a regression project is typically divided into two data sets:

- One for building the model

- One for testing the model

The test data must be compatible with the data used to build the model and must be prepared in the same way that the build data was prepared.

The ways to test Classification and Regression models:

- By splitting the input data into build data and test data. This is the default. The test data is created by randomly splitting the build data into two subsets. 40 percent of the input data is used for test data.

- By using all the build data as the test data.

- By attaching two Data Source nodes to the build node.

- The first data source, that you connect to the build node, is the source of build data.

- The second node that you connect is the source of test data.

- By deselecting Perform Test in the Test section of the Properties pane and then using a Test node. By default, all Classification and Regression models are tested.

Test settings specify which metrics to calculate and control the calculation of the metrics.

Oracle Data Mining provides several kinds of information to assess Regression models:

- Residual Plot

- Regression Statistics

- Regression Model Test Viewer

- Compare Regression test Results

To view test results, first test the model or models in the node:

- If you tested the models using the default test in the Regression node, then run the node and then right-click the node. Select View Test Results and select the model that you are interest in. The Regression Model Test viewer opens. To compare the test results for all models in the node, select Compare Test Results.

- If you tested the models using a Test node, then run the Test node and then right-click the node. Select View Test Results and select the model that you are interested in. The Regression Model Test viewer opens. To compare the test results for all models in the node, select Compare Test Results.

You can also compare test results by going to the Models section of the Properties pane of the Build node where you tested the models and click .

- Residual Plot
The residual plot is a scatter plot of the residuals.

- Regression Statistics
Oracle Data Mining calculates the statistics Root Mean Squared Error and Mean Absolute Error to help the assessment of the overall quality of Regressions models.

- Compare Regression Test Results
You can compare the results of a Regression test for all models that are in a Regression node as well as in a Test node.

- Regression Model Test Viewer
You can view the results of a regression model test in the Regression Model Test Viewer.

Residual Plot

The residual plot is a scatter plot of the residuals.

Each residual is the difference between the actual value and the value predicted by the model. Residuals can be positive or negative. If residuals are small (close to 0), then the predictions are accurate. A residual plot may indicate that predictions are better for some classes of values than others.

Regression Statistics

Oracle Data Mining calculates the statistics Root Mean Squared Error and Mean Absolute Error to help the assessment of the overall quality of Regressions models.

- Root Mean Squared Error: The square root of the average squared distance of a data point from the fitted line.

- Mean Absolute Error: The average of the absolute value of the residuals (error). The Mean Absolute Error is very similar to the Root Mean Square Error but is less sensitive to large errors.

Compare Regression Test Results

You can compare the results of a Regression test for all models that are in a Regression node as well as in a Test node.

To compare test results for all the models in a Regression Build node:

- If you tested the models when you ran the Regression node, then:

- Right-click the Regression node that contains the models.

- Select Compare Test Results.

- If you tested the Regression models in a Test node, then:

- Right-click the Test node that tests the models.

- Select Compare Test Results.

- Compare Test Results
When you compare test results for two or more Regression models, each model has a color associated with it. This color indicates the results for that model.

Compare Test Results

When you compare test results for two or more Regression models, each model has a color associated with it. This color indicates the results for that model.

For example, if model M1 has purple associated with it, then the bar graphs on the Performance tab for M1 is displayed in purple.

By default, test results for all models in the node are compared. If you do not want to compare all test results, then click . The Edit Test Results Selection dialog box opens. Deselect results that you do not want to see. Click OK when you have finished.

Compare Test Results opens in a new tab. Results are displayed in two tabs:

- Performance tab: The following metrics are compared on the Performance tab:

- Predictive Confidence for Classification Models

- Mean Absolute Error

- Mean Predicted Value

By default, test results for all models are compared. To edit the list of models, click above pane that lists the models to open the Edit Test Selection (Classification and Regression dialog box.

- Residual tab: Displays the residual plot for each model.

- You can compare two plots side by side. By default, test results for all models are compared.

- To edit the list of models, click above pane that lists the models to open the Edit Test Selection (Classification and Regression dialog box.

Related Topics

- Regression Statistics

- Predictive Confidence

- Edit Test Selection (Classification and Regression)

Regression Model Test Viewer

You can view the results of a regression model test in the Regression Model Test Viewer.

To view information in the Regression Model Test Viewer:

1. Right-click a Regression node or a Test node (that tests Regression Models) and select View Test Results or Compare Test Results.

2. The Regression Model Test Viewer opens, and displays the following tabs:

- Performance

- Residual

3. Click OK.

- Performance (Regression)
The Performance tab displays the test results for several common test metrics. For Regression models, it displays the measures for all models:

- Residual
The Residual Plot tab show the residual plot on a per-model basis.

Related Topics

- Regression Statistics

Performance (Regression)

The Performance tab displays the test results for several common test metrics. For Regression models, it displays the measures for all models:

The test metrics are:

- All Measures (default). The Measure list enables you to select the measures to display. By default, All Measures are displayed. The selected measures are displayed as graphs. If you are comparing test results for two or more models, then the different models have graphs in different colors.

- Predictive Confidence: Measures how much better the predictions of the mode are than those of the naive model. Predictive Confidence for regression is the same measure as Predictive Confidence for classification.

- Mean Absolute Error

- Root Mean Square Error

- Mean Predicted Value: The average of the predicted values.

- Mean Actual Value: The average of the actual values.

Two Sort By lists specify sort attribute and sort order. The first Sort By list contains Measure, Creation Date, or Name (the default). The second Sort By list contains the sort order: ascending or descending (default).

The top pane displays these measures as histograms.

The bottom pane contains the Models grid that supplements the information presented in the graphs. You can minimize the table using the splitter line.

The Models grid has the following columns:

- Name, the name of the model along with color of the model in the graphs.

- Predictive Confidence

- Mean Absolute Error

- Root Mean Square Error

- Mean Predicted Value

- Mean Actual Value

- Algorithm

- Creation Date (and time)

By default, results for the selected model are displayed. To change the list of models, click and deselect any models for which you do not want to see results. If you deselect a model, then both the histograms and the detail information for that model are not displayed.

Related Topics

- Predictive Confidence

- Regression Statistics

Residual

The Residual Plot tab show the residual plot on a per-model basis.

By default, the residual plots are displayed as graph.

- To see numeric results, click .

- To change the display back to a graph, click .

- To see the plot for another model, select the model from the Show list and click Query.

You can control how the plot is displayed in several ways:

- Select the information displayed on the y-axis and on the x-axis. The default depictions are:

- X axis: Predicted Value

- Y axis: Residual

To change this, select information from the lists.

- The default sample size is 2000. You can make this value larger or smaller.

- You can compare plots side by side. The default is to not compare plots.

If you change any of these fields, then click Query to see the results.

To compare plots side by side, select the model to compare with the current model from the Compare list and click Query. The residual plots are displayed side by side.

The bottom pane show the Residual result summary table. The table contains the Models grid which supplements the information presented in the plots. You can minimize the table using the splitter line.

The table has the following columns:

- Model, the name of the model along with color of the model in the graphs

- Predictive Confidence

- Mean Absolute Error

- Root Mean Square Error

- Mean Predicted Value

- Mean Actual Value

- Algorithm

- Creation Date (and time)

By default, results for all models in the node are displayed. To change the list of models, click to open the Edit Test Selection dialog box.

Related Topics

- Residual Plot

- Edit Test Selection (Classification and Regression)

13 Data Mining Algorithms

The models in Oracle Data Miner are supported by different data mining algorithms.

The algorithms supported by Oracle Data Miner are:

- Anomaly Detection
Anomaly Detection (AD) identifies cases that are unusual within data that is apparently homogeneous.

- Association
Association is an unsupervised mining function for discovering association rules, that is predictions of items, that are likely to be grouped together. Oracle Data Mining provides one algorithm, Association Rules (AR).

- Decision Tree
The Decision Tree algorithm is a Classification algorithm that generates rules. Oracle Data Mining supports the Decision Tree (DT) algorithm.

- Expectation Maximization
Expectation Maximization (EM) is a density estimation technique. Oracle Data Mining implements EM as a distribution-based clustering algorithm that uses probability density estimation.

- Explicit Semantic Analysis
Explicit Semantic Analysis algorithm uses the concepts of an existing knowledge base as features instead of latent features derived by latent semantic analysis methods such as Singular Value Decomposition.

- Generalized Linear Models
Generalized Linear Models (GLM) is a statistical technique for linear modeling. Oracle Data Mining supports GLM for both Regression and Classification.

- k-Means
The k-Means (KM) algorithm is a distance-based clustering algorithm that partitions the data into a predetermined number of clusters, provided there are enough distinct cases.

- Naive Bayes
The Naive Bayes (NB) algorithm is used to build Classification models. You can build, test, apply, and tune a Naive Bayes model.

- Nonnegative Matrix Factorization
Nonnegative Matrix Factorization (NMF) is the unsupervised algorithm used by Oracle Data Mining for feature extraction.

- Orthogonal Partitioning Clustering
Orthogonal Partitioning Clustering is a clustering algorithm that is proprietary to Oracle.

- Singular Value Decomposition and Principal Components Analysis
Singular Value Decomposition (SVD) and Principal Component Analysis (PCA) are unsupervised algorithms used by Oracle Data Mining for feature extraction.

- Support Vector Machine
You can use the Support Vector Machine (SVM) algorithm to build Classification, Regression, and Anomaly Detection models.

- Settings Information
Certain settings related to automatic data preparation. Epsilon Value, Support and Confidence are common to most algorithms.

Related Topics

- Oracle Data Mining Concepts

See Also:
Oracle Data Mining Concepts for more information about data mining functions, data preparation, scoring, and data mining algorithms.

Anomaly Detection

Anomaly Detection (AD) identifies cases that are unusual within data that is apparently homogeneous.

Anomaly detection is an important tool for fraud detection, network intrusion, and other rare events that may have great significance but are hard to find.

Oracle Data Mining uses Support Vector Machine (SVM) as the one-class classifier for Anomaly Detection (AD). When SVM is used for anomaly detection, it has the classification mining function but no target.

There are two ways to search for anomalies:

- By building and applying an Anomaly Detection model. To build an AD model, use an Anomaly Detection node, connected to an appropriate Data Source node.

- By using an Anomaly Detection query, one of the Predictive Query nodes.

- Applying Anomaly Detection Models
Oracle Data Mining uses Support Vector Machine (SVM) as the one-class classifier for Anomaly Detection (AD).

- Algorithm Settings for AD
The algorithm for Anomaly Detection is one-class SVM.

- Anomaly Detection Model Viewer
The information displayed in the model viewer depends on which kernel was used to build the model.

- Viewing Models in Model Viewer
After you build a model successfully, you can view the model details in the Model Viewer.

Related Topics

- Support Vector Machine Algorithms

- Anomaly Detection Node

- Anomaly Detection Query

Applying Anomaly Detection Models

Oracle Data Mining uses Support Vector Machine (SVM) as the one-class classifier for Anomaly Detection (AD).

When SVM is used for Anomaly Detection, it has the Classification mining function but no target. One-class SVM models, when applied, produce a prediction and a probability for each case in the scoring data.

- If the prediction is 1, then the case is considered Typical.

- If the prediction is 0, then the case is considered Anomalous.

This behavior reflects the fact that the model is trained with normal data.

Algorithm Settings for AD

The algorithm for Anomaly Detection is one-class SVM.

The kernel setting is one of the following:

- System Determined (Default)

- Gaussian

- Linear

The settings that you can specify for any version of the Support Vector Machine (SVM) algorithm depend on which of the SVM kernel function that you select.

Note:

After the model is built, the kernel function used (Linear or Gaussian) is displayed in Kernel Function in Algorithm Settings.

- Anomaly Detection Algorithm Settings for Linear or System Determined Kernel
The Anomaly Detection algorithm settings for linear kernel or system determined kernel include Tolerance Value, Complexity Factor, Rate of Outliers, and Active Learning.

- Anomaly Detection Algorithm Settings for Gaussian Kernel
The Anomaly Detection algorithm settings for Gaussian kernel include Tolerance Value, Complexity Factor, Rate of Outliers, Active Learning, Standard Deviation, and Cache Size.

Related Topics

- SVM Kernel Functions

Anomaly Detection Algorithm Settings for Linear or System Determined Kernel

The Anomaly Detection algorithm settings for linear kernel or system determined kernel include Tolerance Value, Complexity Factor, Rate of Outliers, and Active Learning.

If you specify a linear kernel or if you let the system determine the kernel, then you can change the following settings:

- Active Learning
Active Learning is a methodology optimizes the selection of a subset of the support vectors that maintain accuracy while enhancing the speed of the model.

- Complexity Factor
The complexity factor determines the trade-off between minimizing model error on the training data and minimizing model complexity.

- Rate of Outliers
The rate of outliers is the approximate rate of outliers (negative predictions) produced by a one-class SVM model on the training data. This rate indicates the percent of suspicious records.

- Tolerance Value
Tolerance Value is the maximum size of a violation of convergence criteria such that the model is considered to have converged.

Active Learning

Active Learning is a methodology optimizes the selection of a subset of the support vectors that maintain accuracy while enhancing the speed of the model.

Note:
Active Learning is not supported in Oracle Data Miner 17.3 connected to Oracle Database 12.2 and later.

The key features of Active Learning are:

- Increases performance for a linear kernel. Active learning both increases performance and reduces the size of the Gaussian kernel. This is an important consideration if memory and temporary disk space are issues.

- Forces the SVM algorithm to restrict learning to the most informative examples and not to attempt to use the entire body of data. Usually, the resulting models have predictive accuracy comparable to that of the standard (exact) SVM model.

You should not disable this setting

Active Learning is selected by default. It can be turned off by deselecting Active Learning.

Complexity Factor

The complexity factor determines the trade-off between minimizing model error on the training data and minimizing model complexity.

Its responsibility is to avoid over-fit (an over-complex model fitting noise in the training data) and under-fit (a model that is too simple). The default is to not specify a complexity factor.

You specify the complexity factor for an SVM model by selecting Specify the complexity factors.

A very large value of the complexity factor places an extreme penalty on errors, so that SVM seeks a perfect separation of target classes. A small value for the complexity factor places a low penalty on errors and high constraints on the model parameters, which can lead to under-fit.

If the histogram of the target attribute is skewed to the left or to the right, then try increasing complexity.

The default is to specify no complexity factor, in which case the system calculates a complexity factor. If you do specify a complexity factor, then specify a positive number. If you specify a complexity factor for Anomaly Detection, then the default is 1.

Rate of Outliers

The rate of outliers is the approximate rate of outliers (negative predictions) produced by a one-class SVM model on the training data. This rate indicates the percent of suspicious records.

The rate is a number greater than 0 and less than or equal to 1. The default value is 0.1.

If you do not want to specify the rate of outliers, then deselect Specify the Rate of Outliers.

Tolerance Value

Tolerance Value is the maximum size of a violation of convergence criteria such that the model is considered to have converged.

The default value is 0.001. Larger values imply faster building but less accurate models.

Anomaly Detection Algorithm Settings for Gaussian Kernel

The Anomaly Detection algorithm settings for Gaussian kernel include Tolerance Value, Complexity Factor, Rate of Outliers, Active Learning, Standard Deviation, and Cache Size.

If you specify the Gaussian Kernel, then you can change the following settings:

- Active Learning
Active Learning is a methodology optimizes the selection of a subset of the support vectors that maintain accuracy while enhancing the speed of the model.

- Cache Size (Gaussian Kernel)
If you select the Gaussian kernel, then you can specify a cache size for the size of the cache used for storing computed kernels during the build operation.

- Complexity Factor
The complexity factor determines the trade-off between minimizing model error on the training data and minimizing model complexity.

- Rate of Outliers
The rate of outliers is the approximate rate of outliers (negative predictions) produced by a one-class SVM model on the training data. This rate indicates the percent of suspicious records.

- Standard Deviation (Gaussian Kernel)
Standard Deviation is a measure that is used to quantify the amount of variation.

- Tolerance Value
Tolerance Value is the maximum size of a violation of convergence criteria such that the model is considered to have converged.

Active Learning

Active Learning is a methodology optimizes the selection of a subset of the support vectors that maintain accuracy while enhancing the speed of the model.

Note:
Active Learning is not supported in Oracle Data Miner 17.3 connected to Oracle Database 12.2 and later.

The key features of Active Learning are:

- Increases performance for a linear kernel. Active learning both increases performance and reduces the size of the Gaussian kernel. This is an important consideration if memory and temporary disk space are issues.

- Forces the SVM algorithm to restrict learning to the most informative examples and not to attempt to use the entire body of data. Usually, the resulting models have predictive accuracy comparable to that of the standard (exact) SVM model.

You should not disable this setting

Active Learning is selected by default. It can be turned off by deselecting Active Learning.

Cache Size (Gaussian Kernel)

If you select the Gaussian kernel, then you can specify a cache size for the size of the cache used for storing computed kernels during the build operation.

The default size is 50 megabytes.

The most expensive operation in building a Gaussian SVM model is the computation of kernels. The general approach taken to build is to converge within a chunk of data at a time, then to test for violators outside of the chunk. The build is complete when there are no more violators within tolerance. The size of the chunk is chosen such that the associated kernels can be maintained in memory in a Kernel Cache. The larger the chunk size, the better the chunk represents the population of training data and the fewer number of times new chunks must be created. Generally, larger caches imply faster builds.

Complexity Factor

The complexity factor determines the trade-off between minimizing model error on the training data and minimizing model complexity.

Its responsibility is to avoid over-fit (an over-complex model fitting noise in the training data) and under-fit (a model that is too simple). The default is to not specify a complexity factor.

You specify the complexity factor for an SVM model by selecting Specify the complexity factors.

A very large value of the complexity factor places an extreme penalty on errors, so that SVM seeks a perfect separation of target classes. A small value for the complexity factor places a low penalty on errors and high constraints on the model parameters, which can lead to under-fit.

If the histogram of the target attribute is skewed to the left or to the right, then try increasing complexity.

The default is to specify no complexity factor, in which case the system calculates a complexity factor. If you do specify a complexity factor, then specify a positive number. If you specify a complexity factor for Anomaly Detection, then the default is 1.

Rate of Outliers

The rate of outliers is the approximate rate of outliers (negative predictions) produced by a one-class SVM model on the training data. This rate indicates the percent of suspicious records.

The rate is a number greater than 0 and less than or equal to 1. The default value is 0.1.

If you do not want to specify the rate of outliers, then deselect Specify the Rate of Outliers.

Standard Deviation (Gaussian Kernel)

Standard Deviation is a measure that is used to quantify the amount of variation.

If you select the Gaussian kernel, then you can specify the standard deviation of the Gaussian kernel. This value must be positive. The default is to not specify the standard deviation.

For Anomaly Detection, if you specify Standard Deviation, then the default is 1.

Tolerance Value

Tolerance Value is the maximum size of a violation of convergence criteria such that the model is considered to have converged.

The default value is 0.001. Larger values imply faster building but less accurate models.

Anomaly Detection Model Viewer

The information displayed in the model viewer depends on which kernel was used to build the model.

The information displayed in the Model viewer depends on:

- If the Gaussian Kernel is used, then there is one tab, Settings.

- If the Linear kernel is used, then there are three tabs: Coefficients, Compare, and Settings.

Anomaly Detection model is a special kind of Support Vector Machine Classification model.

- AD Model Viewer for Gaussian Kernel
The information displayed in the model viewer depends on which kernel was used to build the model.

- Anomaly Detection Algorithm Settings
Anomaly Detection models are built using a special version of the SVM classification, one-class SVM.

- AD Model Viewer for Linear Kernel
The information displayed in the model viewer depends on which kernel was used to build the model.

Related Topics

- SVM Classification Model Viewer

AD Model Viewer for Gaussian Kernel

The information displayed in the model viewer depends on which kernel was used to build the model.

The Model Viewer of an AD model with Gaussian Kernel displays the information in the Inputs tab and Settings tab.

- Settings (AD)
The Settings tab in the Anomaly Detection model viewer displays information under Summary and Inputs.

- Input (AD)
In the Inputs tab, the attributes that are used to build the model are displayed.

Settings (AD)

The Settings tab in the Anomaly Detection model viewer displays information under Summary and Inputs.

The Anomaly Detection Settings tab has the following tabs:

- Summary (AD)
The General Settings under Summary tab describe the characteristics of the model.

- Input (AD)
In the Inputs tab, the attributes that are used to build the model are displayed.

Summary (AD)

The General Settings under Summary tab describe the characteristics of the model.

This includes:

- Owner

- Name

- Type

- Algorithm

- Target Attribute

- Creation Date

- Duration of Model Build

- Comments

Algorithm Settings control the model build. Algorithm setting are specified when the Build node is defined.

Build Details displays computed settings. Computed settings are generated by Oracle Data Mining when the model is created.

Related Topics

- Algorithm Settings for AD

- General Settings

Input (AD)

In the Inputs tab, the attributes that are used to build the model are displayed.

For each attribute the following information is displayed:

- Name: The name of the attribute.

- Data Type: The data type of the attribute

- Mining Type:

- Categorical

- Numerical

- Mixed: Indicates that the input signature column takes on more than one attribute type.

- Partition: Indicates that the input signature column is used as the partitioning key.

- Data Preparation: YES indicates that data preparation was performed. It helps to distinguish between User and Auto Data Preparation (ADP) as ADP could be turned off, but the user could still embedded a transformation. If Data Preparation is indicated as YES, then select the column and click it. Each group can contain an Input and a Reverse Expression. If there is no Reverse Expression, then it is not displayed. If there is no Input, then nothing is displayed. Transformations are displayed in SQL notation.

- Partition Key: YES indicates that the attribute is a partition key.

Input (AD)

In the Inputs tab, the attributes that are used to build the model are displayed.

For each attribute the following information is displayed:

- Name: The name of the attribute.

- Data Type: The data type of the attribute

- Mining Type:

- Categorical

- Numerical

- Mixed: Indicates that the input signature column takes on more than one attribute type.

- Partition: Indicates that the input signature column is used as the partitioning key.

- Data Preparation: YES indicates that data preparation was performed. It helps to distinguish between User and Auto Data Preparation (ADP) as ADP could be turned off, but the user could still embedded a transformation. If Data Preparation is indicated as YES, then select the column and click it. Each group can contain an Input and a Reverse Expression. If there is no Reverse Expression, then it is not displayed. If there is no Input, then nothing is displayed. Transformations are displayed in SQL notation.

- Partition Key: YES indicates that the attribute is a partition key.

Anomaly Detection Algorithm Settings

Anomaly Detection models are built using a special version of the SVM classification, one-class SVM.

The algorithm has these default settings:

- Kernel function: The default is System Determined. After the model is built, the kernel function used (Linear or Gaussian) is displayed.

- Tolerance value: The default is 0.001

- Specify complexity factors: The default is Do Not

- Specify the rate of outliers: The default is 0.1

- Active learning: ON

- Automatic Data Preparation: ON

Related Topics

- Algorithm Settings for AD

AD Model Viewer for Linear Kernel

The information displayed in the model viewer depends on which kernel was used to build the model.

The model viewer of an AD model with a Linear Kernel has these tabs:

- Coefficients (SVMC Linear)
Support Vector Machine Models built with the Linear Kernel have coefficients. The coefficients are real numbers. The number of coefficients may be quite large.

- Settings (AD)
The Settings tab in the Anomaly Detection model viewer displays information under Summary and Inputs.

- Compare (SVMC Linear)
Support Vector Machine Models built with the Linear kernel allow the comparison of target values. You can compare target values.

Coefficients (SVMC Linear)

Support Vector Machine Models built with the Linear Kernel have coefficients. The coefficients are real numbers. The number of coefficients may be quite large.

The Coefficients tab enables you to view SVM coefficients. The viewer supports sorting to specify the order in which coefficients are displayed and filtering to select which coefficients to display.

Coefficients are displayed in the Coefficients Grid. The relative value of coefficients is shown graphically as a bar, with different colors for positive and negative values. For numbers close to zero, the bar may be too small to be displayed.

Settings (AD)

The Settings tab in the Anomaly Detection model viewer displays information under Summary and Inputs.

The Anomaly Detection Settings tab has the following tabs:

Compare (SVMC Linear)

Support Vector Machine Models built with the Linear kernel allow the comparison of target values. You can compare target values.

For selected attributes, Data Miner calculates the propensity, that is, the natural inclination or preference to favor one of two target values. For example, propensity for target value 1 is the propensity to favor target value 1.

To compare target values:

1. Select how to display information:

- Fetch Size: The default fetch size is 1000 attributes. You can change this number.

- Sort by absolute value: This is the default. You can deselect this option.

2. Select two distinct target values to compare:

- Target Value 1: Select the first target value.

- Target Value 2: Select the second target value.

3. Click Query. If you have not changed any defaults, then this step is not necessary.

The following information is displayed in the grid:

- Attribute: The name of the attribute.

- Value: Value of the attribute

- Propensity for Target_Value_1: Propensity to favor Target Value 1.

- Propensity for Target_Value_2: Propensity to favor Target Value 2.

Viewing Models in Model Viewer

After you build a model successfully, you can view the model details in the Model Viewer.

The node where the model is built must be run successfully.

You can access the Model Viewer in two ways. You can access it using the View Model context menu option:

1. Select the workflow node where the model was built.

2. Right-click and select View Models.

3. Select the model to view.

You can also view the models from model properties:

1. Right-click the node where the model was built.

2. Select Go to Properties.

3. In the Models section in Properties, click .

Association

Association is an unsupervised mining function for discovering association rules, that is predictions of items, that are likely to be grouped together. Oracle Data Mining provides one algorithm, Association Rules (AR).

To build an AR model, use an Association node.

Note:

If the model has 0 rules or a very large number of rules, then you may be required to troubleshoot the AR models.

Data for Association Rules (AR) models is usually in transactional form, unlike the data for other kinds of models.

Oracle Data Mining does not support applying (scoring) AR models.

Topics include:

- Calculating Associations
When calculating associations rules, Apriori algorithm calculates the probability of an item being present in a frequent itemset, given that other items are present. It proceeds by identifying the frequent individual items in the database.

- Data for AR Models
Association does not support text. Association Rules are normally used with transactional data, but it can also be applied to single-record case data (similar to other algorithms).

- Troubleshooting AR Models
Association Rules models may generate many rules with very low Support and Confidence.

- AR Model Viewer
The AR model viewer opens in a new tab. The default name of an Association model has ASSOC in the name.

- Viewing Models in Model Viewer
After you build a model successfully, you can view the model details in the Model Viewer.

Related Topics

- Association Node

Calculating Associations

When calculating associations rules, Apriori algorithm calculates the probability of an item being present in a frequent itemset, given that other items are present. It proceeds by identifying the frequent individual items in the database.

An association mining problem can be broken down into two subproblems:

1. Find all combinations of items in a set of transactions that occur with a specified minimum frequency. These combinations are called frequent itemsets.

2. Calculate Association Rules that express the probability of items occurring together within frequent itemsets.

- Itemsets
An itemset comprises one or more items.

- Association Rules
An association rule is of the form IF antecedent THEN consequent.

Itemsets

An itemset comprises one or more items.

The maximum number of items in an itemset is user-specified.

- If the maximum is 2, then all the item pairs are counted.

- If the maximum is greater than 2, then all the item pairs, all the item triples, and all the item combinations up to the specified maximum are counted.

Association rules are calculated from itemsets. Usually, it is desirable to only generate rules from itemsets that are well-represented in the data. Frequent itemsets are those that occur with a minimum frequency specified by the user.

The minimum frequent itemset support is a user-specified percentage that limits the number of itemsets used for association rules. An itemset must appear in at least this percentage of all the transactions if it is to be used as a basis for Association Rules.

Related Topics

- Association Rules

Association Rules

An association rule is of the form IF antecedent THEN consequent.

An association rule states that an item or group of items, the antecedent, implies the presence of another item, the consequent, with some probability. Unlike Decision Tree rules, which predict a target, Association Rules simply express correlation.

The Apriori algorithm calculates rules that express probable relationships between items in frequent itemsets. For example, a rule derived from frequent itemsets containing A, B, and C might state that if A and B are included in a transaction, then C is likely to also be included.

Association rules have Confidence and Support:

- Confidence of an association rule indicates the probability of both the antecedent and the consequent appearing in the same transaction. Confidence is the conditional probability that the consequent occurs given the occurrence of the antecedent. In other words, confidence is the ratio of the rule support to the number of transactions that include the antecedent.

- Support of an association rule indicates how frequently the items in the rule occur together. Support is the ratio of transactions that include all the items in the antecedent and consequent to the number of total transactions.

Data for AR Models

Association does not support text. Association Rules are normally used with transactional data, but it can also be applied to single-record case data (similar to other algorithms).

Native transactional data consists of two columns:

- Case ID, either categorical or numerical Item ID, either categorical or numerical

Transactional data may also include a third column:

- Item value, either categorical or numerical

A typical example of transactional data is market basket data. In market basket data, a case represents a basket that might contain many items. Each item is stored in a separate row, and many rows may be needed to represent a case. The Case ID values do not uniquely identify each row. Transactional data is also called Multirecord Case Data.

When building an Association model, specify the following:

- Item ID: This is the name of the column that contains the items in a transaction.

- Item Value: This is the name of a column that contains a value associated with each item in a transaction. The Item Value column may specify information such as the number of items (for example, three apples) or the type of the item (for example, Macintosh Apples).
The default value for Item Value is Existence. That is, one or more item identified by Item ID is in the basket.

If you select a specific value for Item Value, then you may have to perform appropriate data preparation. The maximum number of distinct values of Item Value is 10. If the specific value for Item Value is greater than 128, then bin the attribute specified in Item Value using a Transform node.

- Support for Text (AR)
In Oracle Data Miner, Association does not support text.

Related Topics

- Text Mining in Oracle Data Mining

Support for Text (AR)

In Oracle Data Miner, Association does not support text.

If the Oracle Data Mining API, supports text, then using it for Association is not recommended.

Troubleshooting AR Models

Association Rules models may generate many rules with very low Support and Confidence.

If you increase Support and Confidence, then you reduce the number of rules generated.

Usually, Confidence should be greater than or equal to Support.

If a model has no rules, then the following message is displayed in the Rules tab of the Model Viewer: Model contains no rules. Consider rebuilding model with lower confidence and support settings.

- Algorithm Settings for Association Rules

Algorithm Settings for Association Rules

To change algorithm settings for an Association node, right-click the node, and select Advanced Settings. Then select the model. The following settings are displayed in the Algorithm settings tab:

1. Right-click the node.

2. Select Advanced Settings.

3. Select the model. The following settings are displayed in the Algorithm Settings tab:

- Maximum rule length: The default is 4

- Minimum Confidence: The default is 10%

- Minimum Support: The default is 1%

4. If no rules were generated, then:

- First, try decreasing Minimum Support.

- If that does not work, then decrease the minimum Confidence value. It may be necessary to specify a much smaller value for either of these values.

5. After you have finished, click OK.

6. Run the node.

Related Topics

- Algorithm Settings

AR Model Viewer

The AR model viewer opens in a new tab. The default name of an Association model has ASSOC in the name.

The AR Model viewer has these tabs:

- AR Rules
An Association Rule states that an item or group of items implies the presence of another item.

- Itemsets
Rules are calculated from itemsets. The Itemsets tab displays information about the itemsets.

- Settings (AR)
AR models are not scoreable, that is, they cannot be applied to new data. Models that are not scoreable do not have an Attributes tab in the model viewer.

AR Rules

An Association Rule states that an item or group of items implies the presence of another item.

Each rule has a probability. Unlike Decision Tree rules, which predict a target, Association Rules simply express correlation.

If an attribute is a nested column, then the full name is displayed as COLUMN_ NAME.SUBNAME. For example, GENDER.MALE. If the attribute is a normal column, then just the column name is displayed.

Oracle Data Mining supports Association Rules that have one or more items in the antecedent (IF part of a rule) and a single item in the consequent (THEN part of the rule). The antecedent may be referred to as the condition, and the consequent as the association.

Rules have Confidence, Support and Lift.

The Rules tab is divided into two sections: Filtering and Sorting upper section, and AR Rules Grid in the lower section. Sorting or filtering that is defined using the settings in the upper section apply to all the rules in the model. Sorting or filtering defined using the settings in the lower section apply to the grid display only.

You can perform the following functions in the Rules tab:

- Sort By: You can specify the order for display of the rules. You can sort rules by:

- Lift, Confidence, Support, or Length

- Ascending or Descending order

Note:
You can sort by Aggregate information only if aggregates were defined on the node.

- Filter: To see filtering options, click More and select Use Filter. The filter table has the following column:

- TYPE: Indicates the type - Metric or Item

- FILTER ON: Double-click and select any one of the following:

- Lift

- Confidence

- Reverse Confidence

- Item Count

- Support

- Support Count

- FILTER FOR: Indicates whether the filter is for the Rule, Antecedent, or Consequent. Double click to edit.

- VALUES: You can set the range of values here. Double click to edit the range of values and click Apply.

- Fetch Size: Association models often generate many rules. Specify the number of rules to examine by clicking Fetch Size. The default is 1000.

- Query: You can query the database using the criteria that you specify. For example, if you change the default sorting order, specify filtering, or change the fetch size, then click Query.

- AR Rules Grid

- AR Rules Display

- Rule Details

- Sorting

- Filtering

- Item Filters

- Add Item Filter

AR Rules Grid

The lower part of the Rules tab displays the retrieved rule in a grid. The following is displayed above the grid:

- Available Rules: The total number of rules in the model.

- Rules Retrieved: The number of rules retrieved by the query, that is, the number of rules retrieved subject to filtering.

- Rule Content: For maximum information, select Name, Subname, and Value; you can select fewer characteristics from the menu. This selection applies to the rules in the grid only. Rule content is smart in the sense that it sets this value to whatever is more appealing given the nature of the model.

Related Topics

- AR Rules Display

AR Rules Display

For each rule, the Rules grid displays the following information:

- ID: Identifier for the rule, a string of integers.

- Condition

- Association

- Lift: A bar is included in the column. The size of the bar is set to scale to the largest lift value provided by the model by any rule.

- Confidence:

- Support:

- Length

- Antecedent Support

- Condition Support. Aggregate columns can be displayed if aggregate are defined. The number of columns that appear by default is controlled by preferences settings for Association rules.

You can perform the following tasks:

- Sort: You can sort the items in the grid by clicking on the title of the column. This sorting applies to the grid only.

- View details: To see details for a rule, click the rule and examine the rule details.

- Determine validity of a rule: To determine if a rule is valid, you must use support and confidence plus lift.

- Choose different itemset display structure for selected rules. To choose a different display structure, select the rule and click . The Choose ItemSet Display Structure dialog box opens.

For more information, including examples of these statistics, see the discussion of evaluating association rules in Oracle Data Mining Concepts

- Lift for AR Rules
Lift can be defined as the Confidence of the combination of items divided by the support of the consequent.

- Confidence for AR Rules
The Confidence of a rule indicates the probability of both the antecedent and the consequent appearing in the same transaction.

- Support for AR Rules
The Support of a rule indicates how frequently the items in the rule occur together.

- Choose ItemSet Display Structure
The Choose ItemSet Display Structure allows you to select different formats to view transactional data in the Rules tab in the AR Model viewer.

Related Topics

- Choose ItemSet Display Structure

- Oracle Data Mining Concepts

Lift for AR Rules

Lift can be defined as the Confidence of the combination of items divided by the support of the consequent.

Support for AR Rules and Confidence for AR rules must be used to determine if a rule is valid. However, there are times when both of these measures may be high, and yet produce a rule that is not useful.

Lift indicates the strength of a rule over the random co-occurrence of the Antecedent and the Consequent, given their individual support. It provides information about the improvement, the increase in probability of the consequent given the antecedent. Lift is defined as follows:

(Rule Support) /(Support(Antecedent) * Support(Consequent))

Related Topics

- Support for AR Rules

- Confidence for AR Rules

Confidence for AR Rules

The Confidence of a rule indicates the probability of both the antecedent and the consequent appearing in the same transaction.

Confidence is the conditional probability of the consequent, given the antecedent.

Association Rules are of the form IF antecedent THEN consequent.

Support for AR Rules

The Support of a rule indicates how frequently the items in the rule occur together.

Support is the ratio of transactions that include all the items in the antecedent and consequent to the number of total transaction.

Association Rules are of the form IF antecedent THEN consequent.

Choose ItemSet Display Structure

The Choose ItemSet Display Structure allows you to select different formats to view transactional data in the Rules tab in the AR Model viewer.

Oracle Data Miner builds AR models that use transaction format only. The transactional data is represented in AR rules as name.subname, where name=column name and subname=item name. The value is not selected, since AR supports aggregation metrics. If you see Value, then it defaults to 1.

If you use Oracle Data Mining API, then you can build a model that uses non-transactional data. In such a case you would only have name filled in and Subname would be empty. Oracle Data Miner can not build such a model, as non-transactional data structure is not a commonly used structure to represent Market Basket Analysis data. But you can reference it through the Model Node and then view the model.

You must create and run an Association Rules model to view the AR Model viewer.

1. In the Rules tab, select a rule in the Rules section.

2. Click in the Rules section.
The Choose ItemSet Display Structure dialog box opens.

3. In the Choose ItemSet Display Structure field, select an option from the drop-down list. The available options are:

- Name

- Subname

- Name = Value

- Name.Subname = Value

- Subname

- Subname = Value

4. Click OK.

Rule Details

The information in the rule grid is displayed in a readable format in the Rule Detail list.

Sorting

The default sort is:

1. Sort by Lift in descending order (default)

2. Sort by Confidence in descending order

3. Sort by Support in descending order

4. Sort by rule length in descending order

The sorting specified here applies to all rules in the model.

Filtering

To see all the filtering options, click More.

You can specify the following:

- Filter: Filter rules are based on values of rule characteristics. You can specify the following:

- Minimum lift

- Minimum support

- Minimum confidence

- Maximum items in rule

- Minimum items in rule

- Fetch Size: It is the maximum number of rows to fetch. The default is 1000. Smaller values result in faster fetches.

- Define item filters to reduce the number of rules returned.

To define a filter, select Use Filter. After you define the filter, click Query.

Related Topics

- Item Filters

Item Filters

Item filters enable you to see only those rules that contain what you are interested in. A rule filter must consider the item as being required for the Association, Condition, or Both. The rule filter uses OR logic for each side of the Rule (Association Collection, Condition Collection). However, the rule filter performs an AND rule across the collection. So, for a Rule to be returned, it must have at least one Association item AND one Condition item.

You can manage Item Filters using these controls:

- To open the Add Item Filter dialog box, click .

- To delete selected item filters, click .

- To change the Filter column of selected rows to Both, click . Both implies Association and Condition.

- To change the Filter column of selected rows to Condition, click .

- To change the Filter column of selected rows to Association, click .

Related Topics

- Add Item Filter

Add Item Filter

To open the Add Item dialog box, click .

The exact information that is displayed depends on the model. For example, if data has different values for the model that you are viewing, then there is a Values column.

Click More to see all possibilities:

- Specify sorting for item filters: the default is to sort by Attribute Descending and then by Support Ascending.

- Specify a name for the filter.

- Change the Fetch Size from the default of 100,000.

- If you made any changes, then click Query to retrieve the attribute or value pairs.

- Filter the retrieved items by name or value.

- Select one or more item in the grid.

- Select how to use items when filtering rules.

Click OK when you have finished defining the filter.

Itemsets

Rules are calculated from itemsets. The Itemsets tab displays information about the itemsets.

If an attribute is a nested column, then the full name is displayed as COLUMN_ NAME.SUBNAME. For example, GENDER.MALE. If the attribute is a normal column, then just the column name is displayed.

Itemsets have Support. Each itemset contains one or more items.

- Sort Itemsets: Sort By specifies the order of itemsets. You can sort itemsets by:

- ID

- Number of Items

- Support in Ascending Order

- Support in Descending Order

By default, itemset is sorted by Support in Descending order. For more sort options, click More. To change the sort order, make changes and then click Query.

- Filter Itemsets

- View Itemset details. Click the itemset to view the details.

The Itemsets tab displays the following information:

- Available Itemsets: The total number of itemsets in the model.

- Itemsets Retrieved: The number of itemsets retrieved by the query. That is, the number of itemsets retrieved subject to filtering.

- Itemset Content: For maximum information, select all three—Name, Subname, and Value. You can select a few characteristics from the menu.

Other Tabs: The AR Model viewer has these other tabs:

- AR Rules

- Settings

- Itemsets Display

- Itemset Details

Related Topics

- AR Rules

- Settings (AR)

Itemsets Display

For each itemset, the Itemsets grid displays the following information:

- ID: Identifier for the itemset, a string of integers

- Items

- Support. A bar in the column illustrates the relative size of the support.

- Number of Items in the itemset

Itemset Details

To see itemset details, select one or more itemsets in the itemsets grid. The information in the itemsets grid is displayed in a more readable format.

Settings (AR)

AR models are not scoreable, that is, they cannot be applied to new data. Models that are not scoreable do not have an Attributes tab in the model viewer.

The Settings tab has the following tabs:

- Summary

Other Tabs:

- Itemsets

- AR Rules

- Summary

Related Topics

- Itemsets

- AR Rules

Summary

The Summary tab contains the following information about the model.

- General: Lists the following:

- Type of Model

- Owner of the model (Classification, Regression, and so on)

- Model Name (the Schema where the model was built)

- Creation Date

- Duration of the Model build (in minutes)

- Model size in MB

- Comments

- Algorithm: Lists the following:

- Automatic Preparation (on or off),

- Minimum Confidence

- Minimum Support

To change these values, right-click the model node and select Advanced Settings.

- Build Details: Lists the following:

- Itemset Counts

- Maximum Support

- Number of Rows

- Rule Count

- Transaction Count

- Algorithm Settings

Related Topics

- General Settings

Algorithm Settings

Association (AR) supports these algorithm settings:

- Maximum Rule Length: The maximum number of attributes in each rule. This number must be an integer between 2 and 20. Higher numbers of rules result in slower builds. The default value is 4.
You can change the number of attributes in a rule, or you can specify no limit for the number of attributes in a rule. Specifying many attributes in each rule increases the number of rules considerably. A good practice is to start with the default and increase this number slowly.

- Minimum Confidence: Confidence indicates how likely it is that these items to occur together in the data. Confidence is the conditional probability that the consequent will occur given the occurrence of the antecedent.
Confidence is a number between 0 and 100 indicating a percentage. High confidence results in a faster build. The default is 10 percent.

- Minimum Support: A number between 0 and 100 indicating a percentage. Support indicates how often these items occur together in the data.
Smaller values for support results in slower builds and requires more system resources. The default is 1 percent.

- Minimum Support Count: Accepts any integer. Default value is 1.

- Minimum Reverse Confidence (%): Accepts any float value. Default is 0.0 %.

Related Topics

- Association Rules

Viewing Models in Model Viewer

After you build a model successfully, you can view the model details in the Model Viewer.

The node where the model is built must be run successfully.

You can access the Model Viewer in two ways. You can access it using the View Model context menu option:

1. Select the workflow node where the model was built.

2. Right-click and select View Models.

3. Select the model to view.

You can also view the models from model properties:

1. Right-click the node where the model was built.

2. Select Go to Properties.

3. In the Models section in Properties, click .

Decision Tree

The Decision Tree algorithm is a Classification algorithm that generates rules. Oracle Data Mining supports the Decision Tree (DT) algorithm.

Topics include:

- Decision Tree Algorithm
The Decision Tree algorithm is based on conditional probabilities.

- Build, Test, and Apply Decision Tree Models
The Decision Tree algorithm manages its own data preparation internally. It does not require pre-treatment of the data.

- Decision Tree Algorithm Settings
Lists the settings supported by the Decision Tree algorithm.

- Decision Tree Model Viewer
The default name of a Decision Tree model has DT in the name. The Tree viewer has two tabs:

Decision Tree Algorithm

The Decision Tree algorithm is based on conditional probabilities.

Unlike Naive Bayes, Decision Trees generate rules. A rule is a conditional statement that can be used by humans and used within a database to identify a set of records.

The Decision Tree algorithm:

- Creates accurate and interpretable models with relatively little user intervention. The algorithm can be used for both binary and multiclass classification problems. The algorithm is fast, both at build time and apply time. The build process for the Decision Tree Algorithm is parallelized. Scoring can be parallelized irrespective of the algorithm.

- Predicts a target value by asking a sequence of questions. At a given stage in the sequence, the question that is asked depends upon the answers to the previous questions. The goal is to ask questions that together uniquely identify specific target values.

Decision Tree scoring is especially fast. The tree structure, created in the model build, is used for a series of simple tests, (typically 2-7). Each test is based on a single predictor. It is a membership test: either IN or NOT IN a list of values (categorical predictor); or LESS THAN or EQUAL TO some value (numeric predictor).

During the model build, the Decision Tree algorithm must repeatedly find the most efficient way to split a set of cases (records) into two child nodes. Oracle Data Mining offers two homogeneity metrics, gini and entropy, for calculating the splits. The default metric is gini.

- Decision Tree Rules
Rules show the basis for the prediction of the model.

Decision Tree Rules

Rules show the basis for the prediction of the model.

Rules provide model transparency, a window on the inner workings of the model. Oracle Data Mining supports a high level of model transparency.

Confidence and Support are used to rank the rules generated by the Decision Tree Algorithm:

- Support: It is the number of records in the training data set that satisfy the rule.

- Confidence: It is the likelihood of the predicted outcome, given that the rule has been satisfied.

Build, Test, and Apply Decision Tree Models

The Decision Tree algorithm manages its own data preparation internally. It does not require pre-treatment of the data.

The Decision Tree is not affected by Automatic Data Preparation.

The Decision Tree interprets missing values as missing at random. The algorithm does not support nested tables and therefore does not support sparse data. You can perform the following tasks with a Decision Tree model:

- Build Decision Tree model: To build a Decision Tree model, use a Classification node. In Oracle Data Mining 12c Release 1(12.1) or later, Decision Tree supports nested data. The Decision Tree supports text for Oracle Database 12c and later, but not for earlier releases.

- Test Decision Tree model: By default, a Classification Node tests all models that it builds. The test data is created by splitting the input data into build and test subsets. You can also test a Decision Tree model using a Test node.

- Tune Decision Tree model: After you build and test a Decision Tree model, you can tune it.

- Apply Decision Tree model: To apply a model, use an Apply node.

Related Topics

- Apply Node

- Testing Classification Models

- Tuning Classification Models

Decision Tree Algorithm Settings

Lists the settings supported by the Decision Tree algorithm.

- Homogeneity Metric:

- Gini (default)

- Entropy

- Maximum Depth: The maximum number of levels of the tree. The default is 7. The value must be an integer in the range 2 to 20.

- Minimum Records in a Node: The minimum number of records in a node. The default is 10. The value must be an integer greater than or equal to 0.

- Minimum Percent of Records in a Node: The default is 0.05. The value must be a number in the range 0 to 10.

- Maximum Number of Supervised Bins: The upper limit for the number of bins per attribute for algorithms that use supervised binning. The default value is 32.

- Minimum Records for a Split: The minimum number of records for a split. The default is 20. The value must be an integer greater than or equal to 0.

- Minimum Percent of Records for a Split: The default is 0.1. The value must be a number in the range 0 to 20.

Decision Tree Model Viewer

The default name of a Decision Tree model has DT in the name. The Tree viewer has two tabs:

- Tree: This tab is displayed by default. Use the Structure window to navigate and analyze the tree. It is split horizontally into two panes:

- The upper pane displays the tree. The root node is at the top of the pane. The following information is displayed for each node of the tree:

- Node number: 0 is the root node.

- Prediction: The predicted target value.

- Support: This is for the prediction.

- Confidence: This is for the prediction.

- A histogram shows the distribution of target values in the node.

- Split: The attribute used to split the node (Leaf nodes do not have splits).

- The lower pane displays rules. To view the rule associated with a node or a link, select the node or link. The rule is displayed in the lower pane. The following information is displayed in the lower pane:

- Rule

- Surrogates

- Target Values

- Settings: Icons and menus at the top of the upper pane control how the tree and its nodes are displayed. You can perform the following tasks:

- Zoom in or zoom out for the tree. You can also select a size from the drop-down list. You can also fit the tree to the window.

- Change the layout to horizontal. The default Layout Type for the tree is vertical.

- Hide the histograms displayed in the node.

- Show less detail.

- Expand all nodes.

- Save Rules

- Save Rules
You can save the Decision Tree or Clustering Rules as a file in your system.

- Settings (DT)
The Settings tab contains information related to the model summary, inputs, target values, cost matrix (if the model is tuned), partition keys (if the model is partitioned) and so on.

Related Topics

- Viewing Models in Model Viewer

- Structure Window

Save Rules

You can save the Decision Tree or Clustering Rules as a file in your system.

To save the algorithm rules:

1. Click Save Rules on the far right of the upper tab. By default, rules are saved for leaf nodes only to the Microsoft Windows Clipboard. You can then paste the rules into any rich document, such as a Microsoft Word document. You can deselect Leaves Only to save all rules.

2. To save rules to a file, click Save to File and specify a file name.

3. Select the location of the file in the Save dialog box. By default, the rules are saved as an HTML file.

4. Click OK.

Settings (DT)

The Settings tab contains information related to the model summary, inputs, target values, cost matrix (if the model is tuned), partition keys (if the model is partitioned) and so on.

In the Partition field, click the partition name. The partition detail is displayed in the Partition Details window.

Click to open the Select Partition dialog box to view filtered partitions based on the Partition keys.

The Settings tab includes:

- DT Summary
The Summary tab displays the following information about the model.

- DT Inputs
The Inputs tabs shows information about those attributes used to build the model.

- Partition Keys
The Partition Keys tab lists the columns that are partitioned.

- DT Target Values
The Target Values tab displays the target attributes, their data types, and the values of each target attribute.

Related Topics

- Select Partition

DT Summary

The Summary tab displays the following information about the model.

- General contains the following information:

- Type of model

- Owner of the model (the Schema where the model was built)

- Model Name

- Creation Date

- Duration of the model Build (in minutes)

- Size of the model (in MB)

- Comments (if the model has any comments)

- Algorithm

- Build Details displays computed settings. Computed settings are generated by Oracle Data Mining when the model is created.

Related Topics

- Decision Tree Algorithm Settings

- General Settings

DT Inputs

The Inputs tabs shows information about those attributes used to build the model.

Oracle Data Miner does not necessarily use all of the attributes in the build data. For example, if the values of an attribute are constant, then that attribute is not used.

For each attribute used to build the model, this tab displays:

- Name

- Data type

- Mining Type: Categorical, Numerical, or text.

- Target: The in this column indicates that the attribute is a target.

- Data Prep

- Data Preparation: YES indicates that data preparation was performed. It helps to distinguish between User and Auto Data Preparation (ADP) as ADP could be turned off, but the user could still embedded a transformation. If Data Preparation is indicated as YES, then select the column and click it. Each group can contain an Input and a Reverse Expression. If there is no Reverse Expression, then it is not displayed. If there is no Input, then nothing is displayed. Transformations are displayed in SQL notation.

- Partition Key: YES indicates that the attribute is a partition key.

Partition Keys

The Partition Keys tab lists the columns that are partitioned.

Along with the partitioned columns, the Partition Keys tab lists the following details:

- Partition Name

- Source

- Data Type

- Value

DT Target Values

The Target Values tab displays the target attributes, their data types, and the values of each target attribute.

Expectation Maximization

Expectation Maximization (EM) is a density estimation technique. Oracle Data Mining implements EM as a distribution-based clustering algorithm that uses probability density estimation.

In density estimation, the goal is to construct a density function that captures how a given population is distributed. The density estimate is based on observed data that represents a sample of the population.

Note:

Expectation Maximization requires Oracle Database 12c and later.

Dense areas are interpreted as components or clusters. Density-based clustering is conceptually different from distance-based clustering (such as k-Means), where emphasis is placed on minimizing intercluster and maximizing the intracluster distances.

The shape of the probability density function used in EM effectively predetermines the shape of the identified clusters. For example, Gaussian density functions can identify single peak symmetric clusters. These clusters are modeled by single components. Clusters of a more complex shape need to be modeled by multiple components. The EM algorithm assigns model components to high-level clusters by default.

- Build and Apply an EM Model
To build and apply an Expectation Maximization model, use a Clustering node and an Apply node respectively.

- EM Algorithm Settings
Lists the settings supported by the Expectation Maximization algorithm.

- EM Model Viewer
You can view and examine an EM Model in an EM Model Viewer.

Build and Apply an EM Model

To build and apply an Expectation Maximization model, use a Clustering node and an Apply node respectively.

To build an EM model, use a Clustering node.

Note:
You must be connected to Oracle Database 12c and later.

To apply an EM model, use an Apply node.

Related Topics

- Apply Node

- Clustering Node

EM Algorithm Settings

Lists the settings supported by the Expectation Maximization algorithm.

The settings are:

- Number of Clusters is the maximum number of leaf clusters generated by the algorithm. EM may return fewer clusters than the number specified, depending on the data. The number of clusters returned by EM cannot be greater than the number of components, which is governed by algorithm-specific settings. Depending on these settings, there may be fewer clusters than components. If component clustering is disabled, then the number of clusters equals the number of components.
The default is system determined. To specify a specific number of clusters, click User specified and enter an integer value.

- Component Clustering is selected by default.
Component Cluster Threshold specifies a dissimilarity threshold value that controls the clustering of EM components. Smaller values may produce more clusters that are more compact while large values may produce fewer clusters that are more spread out. The default value is 2.

- Linkage Function enables the specification of a linkage function for the agglomerative clustering step. The linkage functions are:

- Single uses the nearest distance within the branch. The clusters tend to be larger and have arbitrary shapes.
Single is the default.

- Average uses the average distance within the branch. There is less chaining effect and the clusters are more compact.

- Complete uses the maximum distance within the branch. The clusters are smaller and require a strong component overlap.

- Approximate Computation indicates whether the algorithm should use approximate computations to improve performance.
For EM, approximate computation is appropriate for large models with many components and for data sets with many columns. The approximate computation uses localized parameter optimization that restricts learning to parameters that are likely to have the most significant impact on the model.

The values for approximate Computation are:

- System Determined (Default)

- Enable

- Disable

- Number of Components specifies the maximum number of components in the model. The algorithm automatically determines the number of components, based on improvements in the likelihood function or based on regularization, up to the specified maximum.
The number of components must be greater than or equal to the number of clusters.

The default number of components is 20.

- Max Number of Iterations specifies the maximum number of iterations in the EM core algorithm. Maximum number of iterations must be greater than or equal to 1. This setting applies to the input table/view as a whole and does not allow per attribute specification.
The default is 100.

- Log Likelihood Improvement specifies the percentage improvement in the value of the log likelihood function required to add a new component to the model.
The default value is 0.001

- Convergence Criterion specifies the convergence criterion for EM. The convergence criteria are:

- System Determined (Default)

- Bayesian Information Criterion

- Held-aside data set

- Numerical Distribution specifies the distribution for modeling numeric attributes. The options are the following distributions:

- Bernoulli

- Gaussian

- System Determined (Default)

When the Bernoulli or Gaussian distribution is chosen, all numerical attributes are modeled using the same distribution. When the distribution is system-determined, individual attributes may use different distributions (either Bernoulli or Gaussian), depending on the data.

- Levels of Details enables or disables the gathering of descriptive statistics for clusters (centroids, histograms, and rules). Disabling the cluster statistics will result in smaller models and will reduce the model details calculated.

- If you select All, then the algorithm settings is enabled.

- If you select Hierarchy, then the algorithm setting is disabled.

- Min Percent of Attribute Rule Support specifies the percentage of the data rows assigned to a cluster that must be present in an attribute to include that attribute in the cluster rule. The default value is 0.1.

- Data Preparation and Analysis specifies settings for data preparation and analysis. To view or change the selections, click Settings.

- Random Seed controls the seed of the random generator used in Expectation Maximization. The value must be a non-negative integer. Default is 0.

- Model Search enables search in EM, where different model sizes are explored and the best size is selected. By default, the setting is set to DISABLE.

- Remove Small Components allows the algorithm to remove very small components from the solution. By default, the setting is set to ENABLE.

Click OK after you are done.

- EM Data Preparation and Analysis Settings

EM Data Preparation and Analysis Settings

This dialog box enables you to view or change these settings:

- Max Number of Correlated 2D Attributes specifies the maximum number of correlated two-dimensional attributes that will be used in the EM model. Two-dimensional attributes correspond to columns that have a simple data type (not nested).
The default is 50.

- Number of Projections per Nested Column specifies the number of projections that will be used for each nested column. If a column has fewer distinct attributes than the specified number of projections, then the data will not be projected. The setting applies to all nested columns.
The default is 50.

- Number of Quantile Bins (Numerical Columns) specifies the number of quantile bins that will be used for modeling numerical columns with multivalued Bernoulli distributions.
The default is system determined.

- Number of Top-N Bins (Categorical Columns) specifies the number of top-N bins that will be used for modeling categorical columns with multivalued Bernoulli distributions.
The default is system determined.

- Number of Equi-Width Bins (Numerical Columns) specifies the number of equi-width bins that will be used for gathering cluster statistics for numerical columns.
The default is 11.

- Include uncorrelated 2D Attributes specifies whether uncorrelated two-dimensional attributes should be included in the model or not. Two-dimensional attributes correspond to columns that are not nested.
The values are:

- System Determined (Default)

- Enable

- Disable

When you have finished making changes, click OK.

EM Model Viewer

You can view and examine an EM Model in an EM Model Viewer.

The Tree tab is displayed by default. The EM model viewer has these tabs:

- EM, KM, and OC Tree Viewer
The Tree Viewer is the graphical tree for hierarchical clusters.

- Cluster (Viewer)
The Cluster tab enables you to view information about a selected cluster. The viewer supports filtering so that only selected probabilities are displayed.

- Cluster Model Settings (Viewer)
The Settings tab in the Cluster Model Viewer contains information related to model summary and model inputs.

- EM, KM, and OC Compare
In the Compare tab, you can compare two clusters in the same model.

- EM Component
The Component tab provides detailed information about the components of the EM model.

- EM Details
The Details tab contains global details about the Expectation Maximization model.

EM, KM, and OC Tree Viewer

The Tree Viewer is the graphical tree for hierarchical clusters.

The tree viewer for Expectation Maximization, k-Means, and Orthogonal Clustering operate in the same way. When you view the tree:

- The Workflow Thumbnail opens to give you a view of the entire tree.

- The Structure window helps you navigate and analyze the tree.

You can compare the attributes in a given node with the attributes in the population using EM, KM, and OC Compare.

To view information about a particular node:

1. Select the node.

2. In the lower pane, the following are displayed in each of these tabs:

- Centroid: Displays the centroid of the cluster

- Cluster Rule: Displays the rule that all elements of the cluster satisfy.

Display Control:

The following control the display of the tree as a whole:

- Zoom in: Zooms in to the diagram, providing a more detailed view of the rule.

- Zoom out: Zooms out from the diagram, providing a view of much or all of the rule.

- Percent size: Enables you select an exact percentage to zoom the view.

- Fit to Window: Zooms out from the diagram until the whole diagram fits within the screen.

- Layout Type: Enables you to select horizontal layout or vertical layout; the default is vertical.

- Expand: All nodes shows branches of the tree.

- Show more detail: Shows more data for each tree node. Click again to show less detail.

- Top Attributes: Displays the top N attributes. N is 5 by default. To change N, select a different number from the list.

- Refresh: Enables you to apply the changed Query Settings.

- Query Settings: Enables you to change the number of top settings. The default is 10. You can save a different number as the new default.

- Save Rules

Related Topics

- Save Rules

- Structure Window

- EM, KM, and OC Compare

- Workflow Thumbnail

Cluster (Viewer)

The Cluster tab enables you to view information about a selected cluster. The viewer supports filtering so that only selected probabilities are displayed.

The Cluster tab for EM, KM, and OC operate in the same way.

The following information is displayed:

- Cluster: The ID of the cluster being viewed. To view another cluster, select a different ID from the menu. You can view Leaves Only (terminal clusters) by selecting Leaves Only. Leaves Only is the default.

- Fetch Size: Default is 20. You can change this value.
If you change Fetch Size, then click Query to see the new display.

The grid lists the attributes in the cluster. For each attribute, the following information is displayed:

- Name of the attribute.

- Histogram of the attribute values in the cluster.

- Confidence displayed as both a number and with a bar indicating a percentage. If confidence is very small, then no bar is displayed.

- Support, the number of cases.

- Mean, displayed for numeric attributes.

- Mode, displayed for categorical attributes.

- Variance

To view a larger version of the histogram, select an attribute; the histogram is displayed below the grid. Place the cursor over a bar in the histogram to see the details of the histogram including the exact value.

You can search the attribute list for a specific attribute name or for a specific value of mode. To search, use the search box.

The drop-down list enables you to search the grid by Attribute (the default) or by Mode. Type the search term in the box next to .

To clear a search, click .

Other Tabs: The NB Model Viewer also has these tabs:

- EM, KM, and OC Tree Viewer

- EM, KM, and OC Compare

- Settings

Related Topics

- EM, KM, and OC Tree Viewer

- EM, KM, and OC Compare

- KM Settings

Cluster Model Settings (Viewer)

The Settings tab in the Cluster Model Viewer contains information related to model summary and model inputs.

The information is available in the following tabs:

EM, KM, and OC Compare

In the Compare tab, you can compare two clusters in the same model.

The Compare tab for EM, KM, and OC operate in the same way. The display enables you to select the two clusters to compare.

You can perform the following tasks:

- Compare Clusters: To select clusters to compare, pick them from the lists. The clusters are compared by comparing attribute values. The comparison is displayed in a grid. You can use Compare to compare an individual cluster with the population.

- Rename Clusters: To rename clusters, click Edit. This opens the Rename Cluster dialog box. By default, only Show Leaves is displayed. To show all nodes, then deselect Leaves Only. The default Fetch Size is 20. You can change this value.

- Search Attribute: To search an attribute, enter its name in the search box. You can also search by rank.

- Create Query: If you make any changes, then click Query.

For each cluster, a histogram is generated that shows the attribute values in the cluster. To see enlarged histograms for a cluster, click the attribute that you are interested in. The enlarged histograms are displayed below the attribute grid.

In some cases, there may be missing histograms in a cluster.

EM Component

The Component tab provides detailed information about the components of the EM model.

The tab is divided into several panes.

The top pane specifies the cluster to view:

- Component: It is the integer that identifies the cluster. The default value is 1.

- Prior: It is the priority for the specified component.

- Filter by Attribute Name: Enables you to display only those attributes of interest. Enter the attribute name, and click Query.

- Fetch Size: It is the number of records fetched. The default is 2,000.

The middle pane displays information about the attributes in the specified component:

- You can search for a specified Attribute using the search box.

- The attributes are displayed in a grid. The grid lists Attribute (name), Distribution (as a histogram), and Mean and Variance (numerical attributes only).
To sort any of these columns, click the column title.

- To see a larger version of the histogram for an attribute and information about the distribution, select the attribute. The histogram is displayed in the bottom pane.

The bottom pane displays a large version of the selected histogram, data, and projections (if any):

- The Chart tab contains a larger version of the histogram of the selected attribute.

- The Data tab shows the frequency of the histogram bins.

- The Projections tab lists projects in a grid, listing Value and Coefficient for each Attribute Subname.

EM Details

The Details tab contains global details about the Expectation Maximization model.

The following information is displayed:

- Log Likelihood Improvement

- Number of Clusters

- Number of Components

Explicit Semantic Analysis

Explicit Semantic Analysis algorithm uses the concepts of an existing knowledge base as features instead of latent features derived by latent semantic analysis methods such as Singular Value Decomposition.

Each concept or feature is represented by an attribute vector or Feature ID. Elements of these attribute vectors quantifies the strength of association between the corresponding attributes and the concept. Elements of the attribute vectors may also be categorical values indicating properties of the concept. Explicit Semantic Analysis creates an inverted index that maps every attribute to knowledge base concepts, that is to a vector of concept-attribute association values. (ESA) is a vectorial representation of text (individual words or entire documents) that uses a document corpus as a knowledge base. In ESA, a word and a document are represented as follows:

- Word: Represented as a column vector in the tf-idf matrix of the text corpus. Typically, the text corpus is Wikipedia.

- Document (string of words): Represented as the centroid of the vectors representing its words.

Oracle Data Mining provides a prebuilt ESA model based on Wikipedia, and user can import the model to Oracle Data Miner for data mining purposes.

- Uses of Algorithm
You can use the Explicit Semantic Analysis (ESA) algorithm in the area of text processing.

- Supported Mining Models
Lists the data mining models supported by Explicit Semantic Algorithm.

- ESA Algorithm Settings
Lists the settings supported by the Explicit Semantic Analysis algorithm.

- ESA Model Viewer
The ESA Model Viewer displays ESA coefficients, alerts, features and algorithm settings.

Uses of Algorithm

You can use the Explicit Semantic Analysis (ESA) algorithm in the area of text processing.

Specific areas of text processing are:

- Document classification

- Semantic related calculations

- Information retrieval

Supported Mining Models

Lists the data mining models supported by Explicit Semantic Algorithm.

The data mining models are:

- Model Node

- Model Details Node

- Apply Node

ESA Algorithm Settings

Lists the settings supported by the Explicit Semantic Analysis algorithm.

The settings are:

- Algorithm Name: Displays the name Explicit Semantic Analysis

- Automatic Preparation: ON (Default). Indicates automatic data preparation.

- Maximum Number of Text Features: Displays the number of text features.

- Minimum Items: Determines the minimum number of non-zero entries that must be present in an input row. The default values are:

- For text inputs: 100

- For non-text inputs: 0

- Minimum number of rows required or Token: Displays the minimum number of rows that is required for a token.

- Missing Values Treatment: If there are missing values in columns with simple data types, then the algorithm replaces missing categorical values with the mode, and missing numerical values with the mean. If there are missing values in nested columns, then the algorithm interprets them as sparse.

- Sampling: Indicates Enabled or Disabled

- Threshold Value: Sets the thresholds value, which is must be very small values in the transformed build data. It must be a non-negative number. The default is 0.00000001

- Top N Feature: Controls the maximum number of features per attribute.

ESA Model Viewer

The ESA Model Viewer displays ESA coefficients, alerts, features and algorithm settings.

The Model Viewer has the following tabs:

- Coefficients: Displays the ESA coefficients. You can specify a Feature ID to search for the coefficients and their attributes

- Features

- Settings

- Alerts: Displays alerts related to partitioned models, if any.

- Features
The Features tab displays all the features along with the Feature IDs and the corresponding items.

- Settings (ESA)
The Settings tab contains generic information about the model, algorithm, inputs and text features in the following tabs.

- Coefficients ESA
Displays the attribute of the selected feature along with their values and coefficients in a tabular format. You can search a feature by specifying the Feature ID.

Related Topics

- Viewing Models in Model Viewer

- ESA Algorithm Settings

Features

The Features tab displays all the features along with the Feature IDs and the corresponding items.

The lower panel contains the following tabs:

- Tag Cloud: Displays the selected feature in a tag cloud format. You can sort the feature tags based on coefficients or alphabetical order. You can also view them in ascending or descending order. To copy and save the cloud image, right-click and select:

- Save Image As

- Copy Image to Clipboard

- Coefficients: Displays the attribute of the selected feature along with their values and coefficients in a tabular format.

Settings (ESA)

The Settings tab contains generic information about the model, algorithm, inputs and text features in the following tabs.

- Summary: Displays information under three categories:

- General: Displays generic information related to the model, such as the model name, model type, creation date, duration and so on.

- Algorithm: Displays information related to the algorithm.

- Build Details: Displays computed settings. Computed settings are generated by Oracle Data Mining when the model is created.

- Inputs: Displays the name, Data Type, Mining Type, Data Preparation, and Partition Key for each attribute.

- Data Preparation: YES indicates that data preparation was performed. It helps to distinguish between User and Auto Data Preparation (ADP) as ADP could be turned off, but the user could still embedded a transformation. If Data Preparation is indicated as YES, then select the column and click it. Each group can contain an Input and a Reverse Expression. If there is no Reverse Expression, then it is not displayed. If there is no Input, then nothing is displayed. Transformations are displayed in SQL notation.

- Partition Key: Yes indicates that the attribute is a partition key

- Text Features: This tab is visible only if text processing takes place. The tab displays words along with the associated document frequencies.

Coefficients ESA

Displays the attribute of the selected feature along with their values and coefficients in a tabular format. You can search a feature by specifying the Feature ID.

- Click . In the Find Values dialog box, enter a feature to search for. You can also provide other parameters to search features.

- Click to query a feature.

Generalized Linear Models

Generalized Linear Models (GLM) is a statistical technique for linear modeling. Oracle Data Mining supports GLM for both Regression and Classification.

The following topics describe GLM models:

- Generalized Linear Models Overview
Generalized Linear Models (GLM) include and extend the class of linear models referred to as Linear Regression.

- GLM Classification Models
Use a Classification node to build, test, and apply a GLM Classification model.

- GLM Classification Algorithm Settings
Lists the settings supported by GLM algorithm.

- GLM Classification Model Viewer
GLMC is also known as Logistic Regression.. The GLM Classification (GLMC) model viewer displays characteristics of a GLMC model.

- GLM Regression Models
Use a Regression node to build, test, and apply a GLM Regression model.

- GLM Regression Algorithm Settings
Lists the settings supported by Generalized Linear Model for Regression.

- GLM Regression Model Viewer
The GLM Regression (GLMR) model viewer displays characteristics of a GLMR model. GLMR is also known as Linear Regression.

Generalized Linear Models Overview

Generalized Linear Models (GLM) include and extend the class of linear models referred to as Linear Regression.

Oracle Data Mining includes two of the most popular members of the GLM family of models with their most popular link and variance functions:

- Linear Regression with the identity link and variance function equal to the constant 1 (constant variance over the range of response values).

- Logistic Regression with the logistic link and binomial variance functions.

In Oracle Database 12c and later, GLM Classification and Regression are enhanced to implement Feature Selection and Feature Generation. This capability, when specified, can enhance the performance of the algorithm and improve the accuracy and interpretability.

- Linear Regression

- Logistic Regression

- Data Preparation for GLM

Linear Regression

Linear Regression is the GLM Regression algorithm supported by Oracle Data Mining. The algorithm assumes no target transformation and constant variance over the range of target values.

Logistic Regression

Binary Logistic Regression is the GLM classification algorithm supported by Oracle Data Mining. The algorithm uses the logit link function and the binomial variance function.

Data Preparation for GLM

Oracle recommends that you use Automatic Data Preparation with GLM.

GLM Classification Models

Use a Classification node to build, test, and apply a GLM Classification model.

You can perform the following tasks with GLM Classification model:

- Build and Test GLM Classification Model: To build and test a GLM Classification (GLMC) model, use a Classification node. By default, the Classification Node tests the models that it builds. Test data is created by splitting the input data into build and test subsets. You can also test a model using a Test node.

- Tune GLM Classification Model: After you build and test a GLM classification model, you can tune it.

- Apply GLM Classification Model: To apply the GLM Classification model, use an Apply node.

Related Topics

- Classification Node

- Testing Classification Models

- Tuning Classification Models

GLM Classification Algorithm Settings

Lists the settings supported by GLM algorithm.

The settings for Classification include:

- Generate Row Diagnostics: By default, Generate Row Diagnostic is deselected. To generate row diagnostics, you must select this option and also specify a Case ID.
If you do not specify a Case ID, then this setting is not available.

You can view Row Diagnostics on the Diagnostics tab in the model viewer. To further analyze row diagnostics, use a Model Details node to extract the row diagnostics table.

- Confidence Level: A positive number that is less than 1.0. This value indicates the degree of certainty that the true probability lies within the confidence bounds computed by the model. The default confidence is 0.95.

- Reference Class name: The Reference Target Class is the target value used as a reference in a binary Logistic Regression model. The probabilities are produced for the other (non-reference) classes. By default, the algorithm chooses the value with the highest prevalence (the most cases). If there are ties, then the attributes are sorted alpha-numerically in ascending order. The default for Reference Class name is System Determined, that is, the algorithm determines the value.

- Missing Values Treatment: The default is Mean Mode, that is, use mean for numeric values and mode for categorical values. You can also select Delete Row to delete any row that contains missing values. If you delete rows with missing values, then the same missing values treatment (delete rows) must be applied to any data that the model is applied to.

- Specify Row Weights Column: By default, Row Weights Column is not specified. The Row Weights Column is a column in the training data that contains a weighting factor for the rows.
Row weights can be used as a compact representation of repeated rows, as in the design of experiments where a specific configuration is repeated several times.

Row weights can also be used to emphasize certain rows during model construction. For example, to bias the model toward rows that are more recent and away from potentially obsolete data.

To specify a Row Weights column, click the check box and select the column from the list.

- Feature Selection: By default, Feature Selection is deselected. This setting requires connection to Oracle Database 12c or later. To specify Feature Selection or view or specify Feature Selection settings, click Option to open the Feature Selection Option dialog box.
If you select Feature Selection, then Ridge Regression is automatically deselected.

Note:

The Feature Selection setting is available in Oracle Database 12c and later.

- Solver: Allows you to choose the GLM Solver. The options are:

- System Determined (Default)

- Stochastic Gradient Descent

- Cholesky

- QR

Note:
This setting is available only if Oracle Data Miner 17.3 is connected to Oracle Database 12.2.

Sparse Solver: By default, this setting is disabled.

- Ridge Regression: By default, Ridge Regression is system determined (not disabled) in both Oracle Database 11g and 12c and later.

Note:

The Ridge Regression setting in both Oracle Database 11g and Oracle Database 12c and later must be consistent (system determined).

If you select Ridge Regression, then Feature Selection is automatically deselected.

Ridge Regression is a technique that compensates for multicollinearity (multivariate regression with correlated predictors). Oracle Data Mining supports Ridge Regression for both regression and classification mining functions.

To specify options for Ridge Regression, click Option to open the Ridge Regression Option dialog box.

When Ridge Regression is enabled, fewer global details are returned. For example, when Ridge Regression is enabled, no prediction bounds are produced.

Note:

If you are connected to Oracle Database 11g Release 2 (11.2) and you get the error ORA-40024 when you build a GLM model, then enable Ridge Regression and rebuild the model.

- Convergence Tolerance: Convergence Tolerance: Determines the convergence tolerance of the GLM algorithm. The value must be in the range 0 to 1, non-inclusive. The default is system determined.

Note:
This setting is available only if Oracle Data Miner 17.3 is connected to Oracle Database 12.2.

- Number of Iterations: Controls the maximum number of iterations for the GLM algorithm. Default is system determined.

Note:
This setting is available only if Oracle Data Miner 17.3 is connected to Oracle Database 12.2.

- Batch Rows: Controls the number of rows in a batch used by the solver. Default is 2000.

Note:
This setting is available only if Oracle Data Miner 17.3 is connected to Oracle Database 12.2.

- Approximate Computation: Specifies whether the algorithm should use approximate computations to improve performance. For GLM, approximation is appropriate for data sets that have many rows and are densely populated (not sparse).
The values for Approximate Computation are:

- System Determined (Default)

- Enable

- Disable

- Feature Selection Option Dialog
The Feature Selection Option dialog box enables you to specify feature selection for a GLMC or a GLMR model.

- Choose Reference Value (GLMC)
You can set the reference value for Generalized Linear Model in the Choose Reference Value dialog box.

- Ridge Regression Option Dialog (GLMC)

Feature Selection Option Dialog

The Feature Selection Option dialog box enables you to specify feature selection for a GLMC or a GLMR model.

In the Algorithm Settings tab, if you select Feature Selection, then Ridge Regression is automatically deselected. Select Feature Selection, and click Option. In the Feature Selection Option dialog box, provide the following settings:

Note:
The setting requires connection to Oracle Database 12c or later.

- Prune Model: By default, Enable is selected. You can also select Disable.

- Max Number of Features: The default setting is system determined.
To specify several features, click User specified and enter an integer number of features.

- Feature Selection Criteria: The default setting is system determined. You can select one of the following:

- Akaike Information

- Schwarz Bayesian Information

- Risk Inflation

- Alpha Investing

- Feature Identification: The default setting is system determined.
You can also choose:

- Enable Sampling

- Disable Sampling

- Feature Acceptance: The default setting is system determined.
You can also choose:

- Strict

- Relaxed

- Categorical Predictor Treatment: By default, Add One at a Time is selected. You can also select Add All at Once.
If you accept the default, that is Add One at a Time, then Feature Generation is not selected. If you select Feature Generation, then the default is Quadratic Candidates. You can also select Cubic Candidates.

Choose Reference Value (GLMC)

You can set the reference value for Generalized Linear Model in the Choose Reference Value dialog box.

To set a reference value for the Generalized Linear Model:

1. In the Advanced Settings dialog box, go to the Algorithm Settings tab.

2. In the Reference Class Name field, click Edit. The Choose Reference Value dialog box opens.

3. In the Choose Reference Value dialog, select Custom. Click the search icon to select a reference value for the model.

4. Select one of the values in the Target Values list.

5. Click OK.

Ridge Regression Option Dialog (GLMC)

You can use the system-determined Ridge Value or you can supply your own. By default, the system determined value is used.

Click OK.

GLM Classification Model Viewer

GLMC is also known as Logistic Regression.. The GLM Classification (GLMC) model viewer displays characteristics of a GLMC model.

To view a GLMC model, use one of these methods:

The viewer has these tabs:

- Details

- Coefficients

- Compare

- Diagnostics. Diagnostics are not generated by default.

- Settings

- GLMC Details
Model Details lists the global metrics for the model as a whole.

- GLMC Coefficients
The Coefficient tab allows you to view GLM coefficients.

- GLMC Compare
GLM Classification Compare viewer is similar to the SVM Coefficients Compare viewer except that the GLM model can only be built for binary classification models.

- GLMC Diagnostics
The Diagnostics tab for GLM Classification displays diagnostics for each Case ID in the build data.

- GLMC Settings
The Settings tab provides information related to model summary, algorithm details, partition details in case of a partitioned models and so on.

Related Topics

- Viewing Models in Model Viewer

GLMC Details

Model Details lists the global metrics for the model as a whole.

The metrics display has two columns: Name of the metric and Value of the metric. The following metrics are displayed:

- Akaike's criterion (AIC) for the fit of the intercept only model

- Akaike's criterion model for the fit of the intercept and the covariates (predictors) mode

- Dependent mean

- Likelihood ratio chi-square value

- Likelihood ratio chi-square probability value

- Likelihood ratio degrees of freedom

- Model converged (Yes or No)

- -2 log likelihood of the intercept only model

- -2 log likelihood of the mode

- Number of parameters (number of coefficients, including the intercept)

- Number of rows

- Correct Prediction percentage

- Incorrectly predicted percentage of rows

- Tied cases prediction, that is, cases where no prediction can be made

- Pseudo R-Square Cox and Snell

- Pseudo R-Square Nagelkerke

- Schwartz's Criterion (SC) for the fit of the intercept-only model

- Schwartz's Criterion for the fit of the intercept and the covariates (predictors) model

- Termination (normal or not)

- Valid covariance matrix (Yes or No)

Note:

The exact list of metrics computed depends on the model settings.

Other Tabs: The viewer also has these tabs:

- Coefficients

- Compare

- Diagnostics (if generated)

- Settings

Related Topics

- GLMC Coefficients

- GLMC Compare

- GLMC Diagnostics

- GLMC Settings

GLMC Coefficients

The Coefficient tab allows you to view GLM coefficients.

The viewer supports sorting to control the order in which coefficients are displayed and filtering to select the coefficients to display.

The default is to sort coefficients by absolute value. If you deselect Sort by Absolute Value, then click Query.

The default fetch size is 1000 records. To change the fetch size, specify a new number of records and click Query.

Note:

After you change any criteria on this tab, click Query to query the database. You must click Query even for changes such as selecting or deselecting sort by absolute value or changing the fetch size.

The relative value of coefficients is shown graphically as a bar, with different colors for positive and negative values. If a coefficient is close to 0, then the bar may be too small to display.

- Target Value: Select a specific target value and see only those coefficients. The default is to display the coefficients of the value that occurs least frequently. It is possible for a target value to have no coefficients; in that case, the list has no entries.

- Sort by absolute value: The default is to sort the list of coefficients by absolute value; you can deselect this option.

- Fetch Size: The number of rows displayed. The default is 1000. To figure out if all coefficients are displayed, choose a fetch size that is greater than the number of rows displayed.

Coefficients are listed in a grid. If no items are listed, then there are no coefficients for that target value. The coefficients grid has these columns:

- Attribute: Name of the attribute

- Value: Value of the attribute

- Coefficient: The linear coefficient estimate for the selected target value is displayed. A bar is shown in front of (and possibly overlapping) each coefficient. The bar indicates the relative size of the coefficient. For positive values, the bar is light blue; for negative values, the bar is red. (If a value is close to 0, then the bar may be too small to be displayed.)

- Standardized coefficient: The coefficient rescaled by the ratio of the standard deviation of the predictor to the standard deviation of the target.
The standardized coefficient places all coefficients on the same scale, so that you can, at a glance, tell the large contributors from the small ones.

- Exp (Coefficient). This is the exponent of the coefficient.

- Standard Error of the estimate.

- Wald Chi Square

- Probability of greater than Chi Square

- Test Statistic: For linear Regression, the t-value of the coefficient estimate; for Logistic Regression, the Wald chi-square value of the coefficient estimate

- Probability of the test statistic. Used to analyze the significance of specific attributes in the model

- Variance Inflation Factor

- 0 for the intercept

- Null for Logistic Regression

- Lower Coefficient Limit, lower confidence bound of the coefficient

- Upper Coefficient Limit, upper confidence bound of the coefficient

- Exp (Coefficient)

- Exponentiated coefficient for Logistic Regression

- Null for Linear Regression

- Exp (Lower Coefficient Limit)

- exponentiation coefficient of the lower confidence bound for Logistic Regression

- Null for Linear Regression

- Exp (Upper Coefficient Limit)

- Exponentiated coefficient of upper confidence bound for Logistic Regression

- Null for Linear Regression

Note:

Not all statistics are necessarily returned for each coefficient.

Statistics are null if any of the following are true:

- The statistic does not apply to the mining function. For example, Exp (coefficient) does not apply to Linear Regression.

- The statistic cannot be calculated because of limitations in system resources.

- The value of the statistics is infinity.

- If the model was built using Ridge Regression, or if the covariance matrix is found to be singular during the build, then coefficient bounds (upper and lower) have the value NULL.

Other Tabs: The viewer also has these tabs:

- Coefficients

- Compare

- Diagnostics

- Settings

- Sort and Search GLMC Coefficients
You can sort numerical columns by clicking the title of the column.

Sort and Search GLMC Coefficients

You can sort numerical columns by clicking the title of the column.

For example, to arrange the coefficients in increasing order, click Coefficients in the grid.

Use to search for items. The default is to search by Attribute (name).

There are search options that limit the columns displayed. The filter settings with the (or)/(and) suffixes enable you to enter multiple strings separated by spaces. For example, if you select Attribute/Value/Coefficient(or), the filter string A .02 produces all columns where the Attribute or the Value Type starts with the letter A or the Coefficient starts with 0.02.

To clear a search, click .

GLMC Compare

GLM Classification Compare viewer is similar to the SVM Coefficients Compare viewer except that the GLM model can only be built for binary classification models.

Only two target class values would be available to compare.

Other Tabs: The viewer has the following tabs:

- Details

- Coefficients

- Diagnostics

- Settings

Related Topics

- GLMC Details

- GLMC Coefficients

- GLMC Diagnostics

- GLMC Settings

GLMC Diagnostics

The Diagnostics tab for GLM Classification displays diagnostics for each Case ID in the build data.

You can filter the results.

Note:

Diagnostics are not generated by default. To generate diagnostics, you must specify a Case ID and select Generate Row Diagnostics in Advanced Settings.

The following information is displayed in the Diagnostics grid:

- CASE_ ID

- TARGET_VALUE for the row in the training data

- TARGET_VALUE_PROB, probability associated with the target value

- HAT, value of diagonal element of the HAT matrix

- WORKING_RESIDUAL, the residual with the adjusted dependent variable

- PEARSON_RESlDUAL, the raw residual scaled by the estimated standard deviation of the target

- DEVIANCE_RESIDUAL, contribution to the overall goodness of the fit of the model

- C, confidence interval displacement diagnostic

- CBAR, confidence interval displacement diagnostic

- DIFDEV, change in the deviance due to deleting an individual observation

- DIFCHISQ, change in the Pearson chi-square

Other Tabs: The viewer has these other tabs:

- Details

- Coefficients

- Compare

- Settings

Related Topics

- GLMC Details

- GLMC Coefficients

- GLMC Compare

- GLMC Settings

GLMC Settings

The Settings tab provides information related to model summary, algorithm details, partition details in case of a partitioned models and so on.

In the Partition field, click the partition name. The partition detail is displayed in the Partition Details window.

Click to open the Select Partition dialog box to view filtered partitions based on the Partition keys.

The Settings tab contains the following tabs:

- Summary
The Summary tab contains information about the characteristics of the model, algorithm settings, and build details.

- Inputs
The Inputs tab displays the list of the attributes used to build the model.

- Partition Keys
The Partition Keys tab lists the columns that are partitioned.

- Weights
The Weights tab displays the weights that are calculated by the system for each target value.

- GLMC Target Values
The Target Values tab displays the target attributes, their data types, and the values of each target attribute.

Related Topics

- Select Partition

Summary

The Summary tab contains information about the characteristics of the model, algorithm settings, and build details.

The General Settings section contains the following information:

- Name

- Type

- Algorithm

- Target Attribute

- Creation Date

- Duration of Model Build

- Comments

Algorithm Settings control model build. Algorithm setting are specified when the Build node is defined.

After a model is built, values calculated by the system are displayed on this tab. For example, if you select System Determined for Enable Ridge Regression, then this tab shows if Ridge Regression was enabled, and what ridge value was calculated.

Build Details displays computed settings. Computed settings are generated by Oracle Data Mining when the model is created.

Other Tabs: The Settings tab has these other tabs:

- Inputs

- Target Values

Related Topics

- Inputs

- GLMC Target Values

- General Settings

- GLM Classification Algorithm Settings

Inputs

The Inputs tab displays the list of the attributes used to build the model.

For each attribute, the following information is displayed:

- Name: The name of the attribute.

- Data Type: The data type of the attribute.

- Mining Type: Categorical or Numerical.

- Target: The icon indicates that the attribute is a target attribute.

- Data Preparation: YES indicates that data preparation was performed. It helps to distinguish between User and Auto Data Preparation (ADP) as ADP could be turned off, but the user could still embedded a transformation. If Data Preparation is indicated as YES, then select the column and click it. Each group can contain an Input and a Reverse Expression. If there is no Reverse Expression, then it is not displayed. If there is no Input, then nothing is displayed. Transformations are displayed in SQL notation.

- Partition Key: YES indicates that the attribute is a partition key.

Partition Keys

The Partition Keys tab lists the columns that are partitioned.

Along with the partitioned columns, the Partition Keys tab lists the following details:

- Partition Name

- Source

- Data Type

- Value

Weights

The Weights tab displays the weights that are calculated by the system for each target value.

If you tune the model, then the weights may change.

GLMC Target Values

The Target Values tab displays the target attributes, their data types, and the values of each target attribute.

Other Tabs: The Settings tab has these other tabs:

- Summary

- Inputs

Related Topics

- Summary

- Inputs

GLM Regression Models

Use a Regression node to build, test, and apply a GLM Regression model.

You can perform the following tasks with GLM Regression models:

- Build and Test GLM Regression model: To build and test a GLM Regression (GLMR) model use a Regression node. By default, a Regression Node tests the models that it builds. Test data is created by splitting the input data into build and test subsets. You can also test a model using a Test node.

- Apply GLM Regression model: To apply a GLM Regression model, use an Apply node.

Related Topics

- Apply Node

- Regression Node

- Test Node

- Testing Regression Models

GLM Regression Algorithm Settings

Lists the settings supported by Generalized Linear Model for Regression.

The settings for Regression are:

- Generate Row Diagnostics is set to OFF by default. To generate row diagnostics, you must select this option and also specify a Case ID.
If you do not specify a Case ID, then this setting is not available.

You can view Row Diagnostics on the Diagnostics tab when you view the model. To further analyze row diagnostics, use a Model Details node to extract the row diagnostics table.

- Confidence Level: A positive number that is less than 1.0. This level indicates the degree of certainty that the true probability lies within the confidence bounds computed by the model. The default confidence is 0.95.

- Missing Values Treatment: The default is Mean Mode. That is, use Mean for numeric values and Mode for categorical values. You can also select Delete Row to delete any row that contains missing values. If you delete rows with missing values, then the same missing values treatment (delete rows) must be applied to any data that the model is applied to.

- Specify Row Weights Column: The Row Weights Column is a column in the training data that contains a weighting factor for the rows. By default, Row Weights Column is not specified. Row weights can be used:

- As a compact representation of repeated rows, as in the design of experiments where a specific configuration is repeated several times.

- To emphasize certain rows during model construction. For example, to bias the model toward rows that are more recent and away from potentially obsolete data

- Feature Selection: This setting requires connection to Oracle Database 12c. By default, Feature Selection is deselected. To specify Feature Selection or view or specify Feature Selection settings, click Option to open the Feature Selection Option dialog box.
If you select Feature Selection, then Ridge Regression is automatically deselected.

Note:

The Feature Selection setting is available only in Oracle Database 12c.

- Solver: Allows you to choose the GLM Solver. The options are:

- System Determined

- Stochastic Gradient Descent

- Cholesky

- QR

Sparse Solver: By default, this setting is disabled.

- Ridge Regression: Ridge Regression is a technique that compensates for multicollinearity (multivariate regression with correlated predictors). Oracle Data Mining supports Ridge Regression for both regression and classification mining functions.
By default, Ridge Regression is system determined (not disabled) in both Oracle Database 11g and Oracle Database 12c. If you select Ridge Regression, then Feature Selection is automatically deselected.

To specify options for Ridge Regression, click Option to open the Ridge Regression Option dialog box.

When Ridge Regression is enabled, fewer global details are returned. For example, when Ridge Regression is enabled, no prediction bounds are produced.

Note:

If you are connected to Oracle Database 11g Release 2 (11.2) and you get the error ORA-40024 when you build a GLM model, then enable Ridge Regression and rebuild the model.

- Convergence Tolerance: Determines the convergence tolerance of the GLM algorithm. The value must be in the range 0 to 1, non-inclusive. The default is system determined.

- Number of Iterations: Controls the maximum number of iterations for the GLM algorithm. Default is system determined.

- Batch Rows: Controls the number of rows in a batch used by the solver. Default is 2000.

- Approximate Computation: Specifies whether the algorithm should use approximate computations to improve performance. For GLM, approximation is appropriate for data sets that have many rows and are densely populated (not sparse).
Values for Approximate Computation are:

- System Determined (Default)

- Enable

- Disable

- Ridge Regression Option Dialog (GLMR)
You can set the ridge value for Generalized Linear Model for Regression in the Ridge Regression Option dialog box.

- Choose Reference Value (GLMR)
You can set the reference value for Generalized Linear Model for Regression in the Choose Reference Value dialog box.

Related Topics

- Feature Selection Option Dialog

Ridge Regression Option Dialog (GLMR)

You can set the ridge value for Generalized Linear Model for Regression in the Ridge Regression Option dialog box.

You can use the System Determined Ridge Value or you can supply your own. By default, the System Determined value is used. Produce Variance Inflation Factor (VIF) is not selected by default.

Choose Reference Value (GLMR)

You can set the reference value for Generalized Linear Model for Regression in the Choose Reference Value dialog box.

To select a value:

1. Click Edit.

2. In the Choose Reference Value dialog box, click Custom.

3. Select one of the values in the Target Values field.

4. Click OK.

GLM Regression Model Viewer

The GLM Regression (GLMR) model viewer displays characteristics of a GLMR model. GLMR is also known as Linear Regression.

The default name of a GLM model has GLM in the name.

The GLMR viewer opens in a new tab.

The Detail tab is displayed by default.

The GLM Regression Model Viewer has these tabs:.

- Details

- Coefficients

- Diagnostics (The default is to not generate diagnostics.)

- Settings

- GLMR Coefficients
You can view the GLM coefficients in the Coefficient tab.

- GLMR Details
The Model Details list global metrics for the model as a whole.

- GLMR Diagnostics
The Diagnostics tab displays diagnostics for each Case ID in the build data.

- GLMR Settings
The Settings tab contains information related to inputs, build details, algorithm settings, and other general settings.

Related Topics

- Viewing Models in Model Viewer

GLMR Coefficients

You can view the GLM coefficients in the Coefficient tab.

The viewer supports sorting to control the order in which coefficients are displayed and filtered to select the coefficients to display.

By default, coefficients are sorted by absolute value. You can deselect or select Sort by absolute value and click Query.

The default fetch size is 1,000 records. To change the fetch size, specify a new number of records and click Query.

Note:

After you change any criteria on this tab, click Query to query the database. You must click Query even for changes such as selecting or deselecting sort by absolute value or changing the fetch size.

The relative value of coefficients is shown graphically as a bar, with different colors for positive and negative values. If a coefficient is close to 0, then the bar may be too small to display.

- Sort by absolute value: Sort the list of coefficients by absolute value.

- Fetch Size: The number of rows displayed. To figure out if all coefficients are displayed, choose a fetch size that is greater than the number of rows displayed.

Coefficients are listed in a grid. If no items are listed, then there are no coefficients for that target value. The coefficients grid has these columns:

- Attribute: Name of the attribute

- Value: Value of the attribute

- Coefficient: The linear coefficient estimate for the selected target value is displayed. A bar is shown in front of (and possible overlapping) each coefficient. The bar indicates the relative size of the coefficient. For positive values, the bar is light blue; for negative values, the bar is red. If a value is close to 0, then the bar may be too small to be displayed.

- Standard Error of the estimate

- Wald Chi Squared

- Pr > Chi Square

- Upper coefficient limit

- Lower coefficient limit

Note:

Not all statistics are necessarily returned for each coefficient.

Statistics are null if any of the following are true:

- The statistic does not apply to the mining function. For example, exp_coefficient does not apply to Linear Regression.

- The statistic cannot be calculated because of limitations in the system resources.

- The value of the statistics is infinity.

- If the model was built using Ridge Regression, or if the covariance matrix is found to be singular during the build, then coefficient bounds (upper and lower) have the value NULL.

Other Tabs: The viewer has these other tabs:

- Details

- Diagnostics

- Settings

Related Topics

- Sort and Search GLMC Coefficients

GLMR Details

The Model Details list global metrics for the model as a whole.

The metrics display has two columns: Name of the metric and Value of the metric. The following metrics are displayed:

- Adjusted R-Square

- Akaike's information criterion

- Coefficient of variation

- Corrected total degrees of freedom

- Corrected total sum of squares

- Dependent mean

- Error degrees of freedom

- Error mean square

- Error sum of squares

- Model F value statistic

- Estimated mean square error

- Hocking Sp statistic

- JP statistic (the final prediction error)

- Model converged (Yes or No)

- Model degrees of freedom

- Model F value probability

- Model mean square

- Model sum of squares

- Number of parameters (the number of coefficients, including the intercept)

- Number of rows

- Root mean square error

- R-square

- Schwartz's Bayesian Information Criterion

- Termination

- Valid covariance matrix computed (Yes or No)

GLMR Diagnostics

The Diagnostics tab displays diagnostics for each Case ID in the build data.

You can filter the results.

Note:

Diagnostics are not generated by default. To generate diagnostics, you must and specify a Case ID and select Generate Row Diagnostics.

The following information is displayed in the Diagnostics grid:

- CASE_ID

- TARGET_VALUE for the row in the training data

- PREDICTED_VALUE, value predicted by the model for the target

- HAT, value of the diagonal element of the HAT matrix

- RESIDUAL, the residual with the adjusted dependent variable

- STD_ERR_RESIDUAL, Standard Error of the residual

- STUDENTIZED_RESIDUAL

- PRED_RES, predicted residual

- COOKS_D, Cook's D influence statistic

Other Tabs: The viewer has these other tabs:

- Details

- Coefficients

- Settings

GLMR Settings

The Settings tab contains information related to inputs, build details, algorithm settings, and other general settings.

The Settings tab has these tabs:

- GLMR Summary
The Summary tab contains information related to general settings, algorithm settings and build details.

- GLMR Inputs
The Inputs tab displays the list of attributes that are used to build the model.

- GLMR Coefficients
You can view the GLM coefficients in the Coefficient tab.

- GLMR Details
The Model Details list global metrics for the model as a whole.

- GLMR Diagnostics
The Diagnostics tab displays diagnostics for each Case ID in the build data.

GLMR Summary

The Summary tab contains information related to general settings, algorithm settings and build details.

- General settings describe the characteristics of the model, including owner, name, type, algorithm, target attribute, creation date duration of model build, and comments.

- Algorithm settings control model build; algorithm setting are specified when the build node is defined. After a model is built, values calculated by the system are displayed on this tab. For example, if you select System Determined for Enable Ridge Regression, then this tab shows if Ridge Regression was enabled and what ridge value was calculated.

- Build Details displays computed settings. Computed settings are generated by Oracle Data Mining when the model is created.

- Settings tab has the GLMR Inputs tab.

Related Topics

- General Settings

- GLM Regression Algorithm Settings

- GLMR Inputs

GLMR Inputs

The Inputs tab displays the list of attributes that are used to build the model.

For each attribute the following information is displayed:

- Name: The name of the attribute.

- Data type: The data type of the attribute.

- Mining type: Categorical or numerical.

- Target: A check mark indicates that the attribute is a target attribute.

- Data Preparation: YES indicates that data preparation was performed. It helps to distinguish between User and Auto Data Preparation (ADP) as ADP could be turned off, but the user could still embedded a transformation. If Data Preparation is indicated as YES, then select the column and click it. Each group can contain an Input and a Reverse Expression. If there is no Reverse Expression, then it is not displayed. If there is no Input, then nothing is displayed. Transformations are displayed in SQL notation.

- Partition Key: YES indicates that the attribute is a partition key.

Related Topics

- GLMR Summary

GLMR Coefficients

You can view the GLM coefficients in the Coefficient tab.

The viewer supports sorting to control the order in which coefficients are displayed and filtered to select the coefficients to display.

By default, coefficients are sorted by absolute value. You can deselect or select Sort by absolute value and click Query.

The default fetch size is 1,000 records. To change the fetch size, specify a new number of records and click Query.

Note:

After you change any criteria on this tab, click Query to query the database. You must click Query even for changes such as selecting or deselecting sort by absolute value or changing the fetch size.

The relative value of coefficients is shown graphically as a bar, with different colors for positive and negative values. If a coefficient is close to 0, then the bar may be too small to display.

- Sort by absolute value: Sort the list of coefficients by absolute value.

- Fetch Size: The number of rows displayed. To figure out if all coefficients are displayed, choose a fetch size that is greater than the number of rows displayed.

Coefficients are listed in a grid. If no items are listed, then there are no coefficients for that target value. The coefficients grid has these columns:

- Attribute: Name of the attribute

- Value: Value of the attribute

- Coefficient: The linear coefficient estimate for the selected target value is displayed. A bar is shown in front of (and possible overlapping) each coefficient. The bar indicates the relative size of the coefficient. For positive values, the bar is light blue; for negative values, the bar is red. If a value is close to 0, then the bar may be too small to be displayed.

- Standard Error of the estimate

- Wald Chi Squared

- Pr > Chi Square

- Upper coefficient limit

- Lower coefficient limit

Note:

Not all statistics are necessarily returned for each coefficient.

Statistics are null if any of the following are true:

- The statistic does not apply to the mining function. For example, exp_coefficient does not apply to Linear Regression.

- The statistic cannot be calculated because of limitations in the system resources.

- The value of the statistics is infinity.

- If the model was built using Ridge Regression, or if the covariance matrix is found to be singular during the build, then coefficient bounds (upper and lower) have the value NULL.

Other Tabs: The viewer has these other tabs:

- Details

- Diagnostics

- Settings

Related Topics

- Sort and Search GLMC Coefficients

GLMR Details

The Model Details list global metrics for the model as a whole.

The metrics display has two columns: Name of the metric and Value of the metric. The following metrics are displayed:

- Adjusted R-Square

- Akaike's information criterion

- Coefficient of variation

- Corrected total degrees of freedom

- Corrected total sum of squares

- Dependent mean

- Error degrees of freedom

- Error mean square

- Error sum of squares

- Model F value statistic

- Estimated mean square error

- Hocking Sp statistic

- JP statistic (the final prediction error)

- Model converged (Yes or No)

- Model degrees of freedom

- Model F value probability

- Model mean square

- Model sum of squares

- Number of parameters (the number of coefficients, including the intercept)

- Number of rows

- Root mean square error

- R-square

- Schwartz's Bayesian Information Criterion

- Termination

- Valid covariance matrix computed (Yes or No)

GLMR Diagnostics

The Diagnostics tab displays diagnostics for each Case ID in the build data.

You can filter the results.

Note:

Diagnostics are not generated by default. To generate diagnostics, you must and specify a Case ID and select Generate Row Diagnostics.

The following information is displayed in the Diagnostics grid:

- CASE_ID

- TARGET_VALUE for the row in the training data

- PREDICTED_VALUE, value predicted by the model for the target

- HAT, value of the diagonal element of the HAT matrix

- RESIDUAL, the residual with the adjusted dependent variable

- STD_ERR_RESIDUAL, Standard Error of the residual

- STUDENTIZED_RESIDUAL

- PRED_RES, predicted residual

- COOKS_D, Cook's D influence statistic

Other Tabs: The viewer has these other tabs:

- Details

- Coefficients

- Settings

k-Means

The k-Means (KM) algorithm is a distance-based clustering algorithm that partitions the data into a predetermined number of clusters, provided there are enough distinct cases.

Distance-based algorithms rely on a distance metric (function) to measure the similarity between data points. The distance metric is either Euclidean, Cosine, or Fast Cosine distance. Data points are assigned to the nearest cluster according to the distance metric used.

To build and apply KM models:

- Use a Clustering node to build KM models.

- Use an Apply node to apply a KM model to new data.

The following topics describe KM models:

- k-Means Algorithm
Oracle Data Mining implements an enhanced version of the k-Means algorithm.

- KM Algorithm Settings
The k-Means (KM) algorithm supports the settings related to number of clusters, growth factor, convergence tolerance, Distance function, number of iterations, and minimum attribute support.

- KM Model Viewer
In the KM Model Viewer, you can examine a KM model.

Related Topics

- Apply Node

- Clustering Node

k-Means Algorithm

Oracle Data Mining implements an enhanced version of the k-Means algorithm.

The features of the k- Means algorithm are:

- The algorithm builds models in a hierarchical manner. The algorithm builds a model top down using binary splits and refinement of all nodes at the end. In this sense, the algorithm is similar to the bisecting k-Means algorithm. The centroid of the inner nodes in the hierarchy are updated to reflect changes as the tree evolves. The whole tree is returned.

- The algorithm grows the tree, one node at a time (unbalanced approach). Based on a user setting, the node with the largest variance is split to increase the size of the tree until the desired number of clusters is reached. The maximum number of clusters is specified as a build setting.

- The algorithm provides probabilistic scoring and assignment of data to clusters.

- The algorithm returns the following, for each cluster:

- A centroid (cluster prototype). The centroid reports the mode for categorical attributes, or the mean and variance for numerical attributes.

- Histograms (one for each attribute),

- A rule describing the hyper box that encloses the majority of the data assigned to the cluster.

The clusters discovered by enhanced k-Means are used to generate a Bayesian probability model that is then used during scoring (model apply) for assigning data points to clusters. The k-Means algorithm can be interpreted as a mixture model where the mixture components are spherical multivariate normal distributions with the same variance for all components.

Note:

The k-Means algorithm samples one million rows. You can use the sample to build the model.

KM Algorithm Settings

The k-Means (KM) algorithm supports the settings related to number of clusters, growth factor, convergence tolerance, Distance function, number of iterations, and minimum attribute support.

The settings and their descriptions are:

- Number of Clusters is the maximum number of leaf clusters generated by the algorithm. The default is 10. k-Means usually produces the exact number of clusters specified, unless there are fewer distinct data points.

- Growth Factor is a number greater than 1 and less than or equal to 5. This value specifies the growth factor for memory allocated to hold cluster data. Default is 2.

- Convergence Tolerance must be between 0.001 (slow build) and 0.1 (fast build). The default is 0.01. The tolerance controls the convergence of the algorithm. The smaller the value, the closest to the optimal solution at the cost of longer run times. This parameter interacts with the number of iterations parameter.

- Distance Function specifies how the algorithm calculates distance. The default distance function is Euclidean. The other distance functions are:

- Cosine

- Fast Cosine

- Number of Iterations must be greater than or equal to 1. The default is 30. This value is the maximum number of iterations for the k-Means algorithm. In general, more iterations result in a slower build. However, the algorithm may reach the maximum, or it may converge early. The convergence is determined by whether the Convergence Tolerance setting is satisfied.

- Min Percent Attribute Support is not an integer. The range of the value for Min Percent Attribute Support is:

- Greater than or equal to 0, and

- Less than or equal to 1.
The default value is 0.1. The default value enables you to highlight the more important predicates instead producing a long list of predicates that have very low support.

You can use this value to filter out rule predicates that do not meet the support threshold. Setting this value too high can result in very short or even empty rules.

In extreme cases, for very sparse data, all attribute predicates may be filtered out so that no rule is produced. If no rule is produced, then you can lower the support threshold and rebuild the model to make the algorithm produce rules even if the predicate support is very low.

- Number of Histogram Bins is a positive integer; the default value is 10. This value specifies the number of bins in the attribute histogram produced by k-Means. The bin boundaries for each attribute are computed globally on the entire training data set. The binning method is equiwidth. All attributes have the same number of bins except attributes with a single value that have only one bin.

- Split Criterion is either Variance or Size. The default is Variance. The split criterion is related to the initialization of the k-Means clusters. The algorithm builds a binary tree and adds one new cluster at a time. Size results in placing the new cluster in the area where the largest current cluster is located. Variance places the new cluster in the area of the most spread out cluster.

- Levels of Details determine the level of cluster detail that will be computed during the build. The applicable values are:

- None: No details. Only scoring information is persisted

- Hierarchy: Cluster Hierarchy and Cluster Record Counts

- All: Cluster Hierarchy, Record Counts, and all descriptive statistics such as Means, Variances, Modes, Histograms, Rules

- Random Seed controls the seed of the random generator used during the k-Means initialization. The random seed must be a value greater than or equal to 1. Default is 0.

KM Model Viewer

In the KM Model Viewer, you can examine a KM model.

The KM model viewer contains these tabs:

- EM, KM, and OC Tree Viewer
The Tree Viewer is the graphical tree for hierarchical clusters.

- Cluster (Viewer)
The Cluster tab enables you to view information about a selected cluster. The viewer supports filtering so that only selected probabilities are displayed.

- EM, KM, and OC Compare
In the Compare tab, you can compare two clusters in the same model.

- KM Settings
The Settings tab displays information about how the model was built

Related Topics

- Viewing Models in Model Viewer

EM, KM, and OC Tree Viewer

The Tree Viewer is the graphical tree for hierarchical clusters.

The tree viewer for Expectation Maximization, k-Means, and Orthogonal Clustering operate in the same way. When you view the tree:

- The Workflow Thumbnail opens to give you a view of the entire tree.

- The Structure window helps you navigate and analyze the tree.

You can compare the attributes in a given node with the attributes in the population using EM, KM, and OC Compare.

To view information about a particular node:

1. Select the node.

2. In the lower pane, the following are displayed in each of these tabs:

- Centroid: Displays the centroid of the cluster

- Cluster Rule: Displays the rule that all elements of the cluster satisfy.

Display Control:

The following control the display of the tree as a whole:

- Zoom in: Zooms in to the diagram, providing a more detailed view of the rule.

- Zoom out: Zooms out from the diagram, providing a view of much or all of the rule.

- Percent size: Enables you select an exact percentage to zoom the view.

- Fit to Window: Zooms out from the diagram until the whole diagram fits within the screen.

- Layout Type: Enables you to select horizontal layout or vertical layout; the default is vertical.

- Expand: All nodes shows branches of the tree.

- Show more detail: Shows more data for each tree node. Click again to show less detail.

- Top Attributes: Displays the top N attributes. N is 5 by default. To change N, select a different number from the list.

- Refresh: Enables you to apply the changed Query Settings.

- Query Settings: Enables you to change the number of top settings. The default is 10. You can save a different number as the new default.

- Save Rules

Related Topics

- Save Rules

- Structure Window

- EM, KM, and OC Compare

- Workflow Thumbnail

Cluster (Viewer)

The Cluster tab enables you to view information about a selected cluster. The viewer supports filtering so that only selected probabilities are displayed.

The Cluster tab for EM, KM, and OC operate in the same way.

The following information is displayed:

- Cluster: The ID of the cluster being viewed. To view another cluster, select a different ID from the menu. You can view Leaves Only (terminal clusters) by selecting Leaves Only. Leaves Only is the default.

- Fetch Size: Default is 20. You can change this value.
If you change Fetch Size, then click Query to see the new display.

The grid lists the attributes in the cluster. For each attribute, the following information is displayed:

- Name of the attribute.

- Histogram of the attribute values in the cluster.

- Confidence displayed as both a number and with a bar indicating a percentage. If confidence is very small, then no bar is displayed.

- Support, the number of cases.

- Mean, displayed for numeric attributes.

- Mode, displayed for categorical attributes.

- Variance

To view a larger version of the histogram, select an attribute; the histogram is displayed below the grid. Place the cursor over a bar in the histogram to see the details of the histogram including the exact value.

You can search the attribute list for a specific attribute name or for a specific value of mode. To search, use the search box.

The drop-down list enables you to search the grid by Attribute (the default) or by Mode. Type the search term in the box next to .

To clear a search, click .

Other Tabs: The NB Model Viewer also has these tabs:

- EM, KM, and OC Tree Viewer

- EM, KM, and OC Compare

- Settings

Related Topics

- EM, KM, and OC Tree Viewer

- EM, KM, and OC Compare

- KM Settings

EM, KM, and OC Compare

In the Compare tab, you can compare two clusters in the same model.

The Compare tab for EM, KM, and OC operate in the same way. The display enables you to select the two clusters to compare.

You can perform the following tasks:

- Compare Clusters: To select clusters to compare, pick them from the lists. The clusters are compared by comparing attribute values. The comparison is displayed in a grid. You can use Compare to compare an individual cluster with the population.

- Rename Clusters: To rename clusters, click Edit. This opens the Rename Cluster dialog box. By default, only Show Leaves is displayed. To show all nodes, then deselect Leaves Only. The default Fetch Size is 20. You can change this value.

- Search Attribute: To search an attribute, enter its name in the search box. You can also search by rank.

- Create Query: If you make any changes, then click Query.

For each cluster, a histogram is generated that shows the attribute values in the cluster. To see enlarged histograms for a cluster, click the attribute that you are interested in. The enlarged histograms are displayed below the attribute grid.

In some cases, there may be missing histograms in a cluster.

- Compare Cluster with Population
You can view how an individual cluster compares with the population.

- Missing Histograms in a Cluster
If clusters are built using sparse data, then some attribute values are not present in the records assigned to a cluster.

- Rename Cluster
Cluster ID is a number. The title bar of the dialog box shows the cluster to rename.

Compare Cluster with Population

You can view how an individual cluster compares with the population.

To compare cluster with the population:

1. Click Compare.

2. Deselect Leaves Only.

3. Select the root node as Cluster 1. This is cluster 1, if the clusters have not been renamed. The distribution of attribute values in Cluster 1 represents the distribution of values in the population as a whole. Select the cluster that you want to compare with the population as Cluster 2.

4. You can now compare the distribution of values for each attribute in the cluster selected as Cluster 2 with the values in Cluster 1.

Missing Histograms in a Cluster

If clusters are built using sparse data, then some attribute values are not present in the records assigned to a cluster.

In this case, a cluster comparison shows the centroid and histogram values for the cluster where the attribute is present and leaves blanks for the cluster where the attribute is present.

Rename Cluster

Cluster ID is a number. The title bar of the dialog box shows the cluster to rename.

You can change it to a string. To rename a cluster:

1. Type in the new name.

2. Click OK.

Note:

Two different clusters cannot have the same name.

KM Settings

The Settings tab displays information about how the model was built

The information is available in the following tabs:

- Cluster Model Summary

- Cluster Model Input

Other Tabs:

- EM, KM, and OC Tree Viewer

- Cluster Viewer

- EM, KM, and OC Compare

- Cluster Model Settings (Viewer)
The Settings tab in the Cluster Model Viewer contains information related to model summary and model inputs.

Related Topics

- Cluster Model Summary

- Cluster Model Input

Cluster Model Settings (Viewer)

The Settings tab in the Cluster Model Viewer contains information related to model summary and model inputs.

The information is available in the following tabs:

- Cluster Model Summary
The Summary tab contains generic information related to the model, model build, and algorithms.

- Cluster Model Input
The Input tab is displayed for models that can be scored only.

Cluster Model Summary

The Summary tab contains generic information related to the model, model build, and algorithms.

The Summary tab lists the following:

- General settings lists the following information:

- Type of Model (Classification, Regression, and so on)

- Owner of the model (the Schema where the model was built)

- Model Name

- Creation Date

- Duration of the Model Build (in minutes)

- Size of the model (in MB)

- Comments

- Algorithm Settings list the algorithm and algorithm settings used to build the model.

- Build Details displays computed settings. Computed settings are generated by Oracle Data Mining when the model is created.

Related Topics

- KM Algorithm Settings

- EM Algorithm Settings

- OC Algorithm Settings

- General Settings

Cluster Model Input

The Input tab is displayed for models that can be scored only.

A list of the attributes used to build the model. For each attribute the following information is displayed:

- Name: The name of the attribute.

- Data Type: The data type of the attribute.

- Mining Type: Categorical or numerical.

- Data Preparation: YES indicates that data preparation was performed. It helps to distinguish between User and Auto Data Preparation (ADP) as ADP could be turned off, but the user could still embedded a transformation. If Data Preparation is indicated as YES, then select the column and click it. Each group can contain an Input and a Reverse Expression. If there is no Reverse Expression, then it is not displayed. If there is no Input, then nothing is displayed. Transformations are displayed in SQL notation.

- Partition Key: YES indicates that the attribute is a partition key.

Naive Bayes

The Naive Bayes (NB) algorithm is used to build Classification models. You can build, test, apply, and tune a Naive Bayes model.

- To build an NB model, use a Classification Node. By default, a Classification Node tests all models that it builds. The test data is created by splitting the input data into build and test subsets.

- To test an NB model, you can also use a Test node.

- To apply an NB model to new data, use an Apply node.

- To tune an NB model, you must first build and test an NB model.

The following topics describe Naive Bayes:

- Naive Bayes Algorithm
The Naive Bayes (NB) algorithm is based on conditional probabilities, and used Bayes; Theorem.

- Naive Bayes Test Viewer
By default, any Classification or Regression model is automatically tested. You have the option to view the test results.

- Naive Bayes Model Viewer
The Naive Bayes Model Viewer allows you examine a Naive Bayes model.

Related Topics

- Apply Node

- Classification Node

- Testing Classification Models

- Testing Regression Models

Naive Bayes Algorithm

The Naive Bayes (NB) algorithm is based on conditional probabilities, and used Bayes; Theorem.

Naive Bayes (NB) algorithm uses Bayes' Theorem, which calculates a probability by counting the frequency of values and combinations of values in the historical data. Bayes' Theorem finds the probability of an event occurring given the probability of another event that has already occurred.

Assumption:

Naive Bayes makes the assumption that each predictor is conditionally independent of the others. For a given target value, the distribution of each predictor is independent of the other predictors. In practice, this assumption of independence, even when violated, does not degrade the model's predictive accuracy significantly, and makes the difference between a fast, computationally feasible algorithm and an intractable one.

Sometimes, the distribution of a given predictor is clearly not representative of the larger population. For example, there might be only a few customers under 21 in the training data, but in fact, there are many customers in this age group in the wider customer base. To compensate, you can specify prior probabilities (priors) when training the model.

- Advantages of Naive Bayes

Advantages of Naive Bayes

The advantages of Naive Bayes model are:

- The Naive Bayes algorithm provides fast, highly scalable model building and scoring. It scales linearly with the number of predictors and rows.

- The Naive Bayes build process is parallelized. Scoring can also be parallelized irrespective of the algorithm.

- Naive Bayes can be used for both binary and multiclass classification problem

Naive Bayes Test Viewer

By default, any Classification or Regression model is automatically tested. You have the option to view the test results.

A Classification model is tested by comparing the predictions of the model with known results. Oracle Data Miner keeps the latest test result.

To view the test results for a model, right-click the Build node and select View Results.

Related Topics

- Testing Classification Models

Naive Bayes Model Viewer

The Naive Bayes Model Viewer allows you examine a Naive Bayes model.

You can view a Naive Bayes model using any one of the following methods.

The NB model viewer has these tabs:

- Probabilities (NB)
The Probabilities tab lists the probabilities calculated during model build. You can sort and filter the order in which probabilities are displayed.

- Compare (NB)
The Compare tab enables you to compare results for two different target values.

- Settings (NB)
The Settings tab contains information related to the model summary, inputs, target values, cost matrix (if the model is tuned), partition keys (if the model is partitioned) and so on.

Related Topics

- Viewing Models in Model Viewer

Probabilities (NB)

The Probabilities tab lists the probabilities calculated during model build. You can sort and filter the order in which probabilities are displayed.

The relative value of probabilities is shown graphically as a bar, with a blue bar for positive values and red bar for negative values. For numbers close to zero, the bar may be too small to be displayed.

Select Target Value. The probabilities associated with the selected value are displayed. The default is to display the probabilities for the value that occurs least frequently.

Probabilities are listed in the grid.

Other Tabs: The NB Model Viewer has these other tabs:

- Compare

- Settings

- Grid
In the grid, you can view the row count and the grid filter.

- Fetch Size
Fetch Size limits the number of rows returned regardless of Filter or Server settings.

- Grid Filter
In the grid filter, you can filter items according to different categories.

Related Topics

- Compare (NB)

- Settings (NB)

Grid

In the grid, you can view the row count and the grid filter.

If no items are listed, then there are no values that satisfy the criteria that you specified:

- Row Count: The number of rows displayed.

- Grid Filter: Use the Grid Filter to filter the information in the grid.

The probabilities grid has these columns:

- Attribute: Name of the attribute

- Value: Value of the attribute

- Probability: The probability for the value of the attribute. Probability is displayed as both a number and with a bar indicating a percentage. If the probability is very small, then no bar is displayed.

Related Topics

- Grid Filter

Fetch Size

Fetch Size limits the number of rows returned regardless of Filter or Server settings.

The default fetch size is 1000. Change the Fetch Size by clicking the up or down arrows. If you change the Fetch Size, then click Query.

Grid Filter

In the grid filter, you can filter items according to different categories.

The filter control enables you to filter the items that are displayed in the grid. The filtering is done as you type in the filter search box.

To see the filter categories, click the down arrow next to the binoculars icon. The following categories are supported for probabilities:

- Attribute: Filters the Attribute (name) column. This is the default category. For example, to display all entries with CUST in the attribute name, enter CUST in the search box.

- Value: Filters the value column.

- Probability: Filters the probability column.

- All (And): Enter in one or more strings and their values are compared against the Attribute and Value columns using the AND condition. For example, enter CUST M to display rows where the attribute name contains CUST and the value is M.

- All (Or): Works the same as All (And) except that the comparison uses an OR condition.

The Grid Filter for Compare lists similar categories:

- Name: Filters by attribute name (Default).

- Value: Filters the value column.

- Attribute/Value/Propensity (or): Filters for values in any of the attribute, value, and propensity columns.

- Attribute/Value/Propensity (and): Filters for values in any of the attribute, value, and propensity columns.

- Propensity for Target Value 1: Filters the propensity values for Target Value 1.

- Propensity for Target Value 2: Filters the propensity values for Target Value 2.

After you enter one or more strings into the filter search box, is displayed. Click this icon to clear the search string.

Compare (NB)

The Compare tab enables you to compare results for two different target values.

Select the two target values. The default values for Target Value 1 and Target Value 2 are displayed.

You can do the following:

- Change the Target Values. The Target Values that you select must be different.

- Use the Grid Filter to display specific values.

- Change the Fetch Size.

- Sort the grid columns. The grid for compare has these columns:

- Attribute: Name of the attribute

- Value:Value of the attribute

- Propensity for target value 1

- Propensity for target value 2

For both propensities, a histogram bar is displayed. The maximum value of propensity is 1.0. The minimum is -1.0.

Propensity shows which of the two target values has a more predictive relationship for a given attribute value pair. Propensity can be measured in terms of being predicted for or against a target value, where prediction against is shown as a negative value.

Other Tabs:

- Probabilities

- Settings

Related Topics

- Grid Filter

- Fetch Size

Settings (NB)

The Settings tab contains information related to the model summary, inputs, target values, cost matrix (if the model is tuned), partition keys (if the model is partitioned) and so on.

In the Partition field, click the partition name. The partition detail is displayed in the Partition Details window.

Click to open the Select Partition dialog box.

The Settings tab displays information about how the model was built:

Other Tabs: The NB Model Viewer has these other tabs:

- Compare

- Probabilities

- Settings (NB)
The Settings tab shows information about the model.

- Summary (NB)
The Summary tab describes all model settings.

- Input (NB)
The Input tab for Naive Bayes is displayed for models that can be scored only.

- Partition Keys
The Partition Keys tab lists the columns that are partitioned.

- Weights
The Weights tab displays the weights that are calculated by the system for each target value.

- Target Values
The Target Values tab displays the target attributes, their data types, and the values of each target attribute.

Settings (NB)

The Settings tab shows information about the model.

The Settings tab has these tabs:

- Summary (NB)
The Summary tab describes all model settings.

- Input (NB)
The Input tab for Naive Bayes is displayed for models that can be scored only.

- Weights
The Weights tab displays the weights that are calculated by the system for each target value.

- Target Values
The Target Values tab displays the target attributes, their data types, and the values of each target attribute.

- Target Values
The Target Values tab displays the target attributes, their data types, and the values of each target attribute.

Summary (NB)

The Summary tab describes all model settings.

Model settings describe characteristics of model building. The Settings are divided into:

Input (NB)

The Input tab for Naive Bayes is displayed for models that can be scored only.

A list of the attributes used to build the model. For each attribute the following information is displayed:

- Name: The name of the attribute.

- Data Type: The data type of the attribute.

- Mining Type: Categorical or Numerical.

- Target: The icon indicates that the attribute is a target attribute.

- Data Preparation: YES indicates that data preparation was performed. It helps to distinguish between User and Auto Data Preparation (ADP) as ADP could be turned off, but the user could still embedded a transformation. If Data Preparation is indicated as YES, then select the column and click it. Each group can contain an Input and a Reverse Expression. If there is no Reverse Expression, then it is not displayed. If there is no Input, then nothing is displayed. Transformations are displayed in SQL notation.

- Partition Key: YES indicates that the attribute is a partition key.

Weights

The Weights tab displays the weights that are calculated by the system for each target value.

If you tune the model, then the weights may change.

Target Values

The Target Values tab displays the target attributes, their data types, and the values of each target attribute.

The Target Values tab for Naive Bayes displays the following:

- Target Attributes

- Data Types

- Values of each target attributes

Target Values

The Target Values tab displays the target attributes, their data types, and the values of each target attribute.

The Target Values tab for Naive Bayes displays the following:

- Target Attributes

- Data Types

- Values of each target attributes

Summary (NB)

The Summary tab describes all model settings.

Model settings describe characteristics of model building. The Settings are divided into:

- Naive Bayes Algorithm Settings
Lists the settings for Naive Bayes algorithm.

- General Settings
The generic settings are contained in the Settings tab and General tab.

Naive Bayes Algorithm Settings

Lists the settings for Naive Bayes algorithm.

This section identifies the algorithm and whether Automatic Data Preparation is ON or OFF.

These settings are specific to Naive Bayes:

- Pair wise Threshold: The minimum percentage of pair wise occurrences required for including a predictor in the model. The default is 0.

- Singleton Threshold: The minimum percentage of singleton occurrences required for including a predictor in the model. The default is 0.

Related Topics

- Automatic Data Preparation (ADP)

General Settings

The generic settings are contained in the Settings tab and General tab.

The Settings tab of a model viewer displays settings in three categories:

- General displays generic information about the model, as described in this topic.

- Algorithm Settings displays information that are specific to the selected algorithm.

- Build Details displays computed settings. Computed settings are generated by Oracle Data Mining when the model is created.

The General tab contains the following information for all algorithms:

- Type The mining function for the model: anomaly detection, association rules, attribute importance, classification, clustering, feature extraction, or regression.

- Owner: The data mining account (schema) used to build the model.

- Model Name: The name of the model.

- Target Attribute: The target attribute; only Classification and Regression models have targets.

- Creation Date: The date when the model was created in the form MM/DD/YYYY

- Duration: Time in minutes required to build model.

- Size: The size of the model in megabytes.

- Comment: For models not created using Oracle Data Miner, this option displays comments embedded in the models. To see comments for models built using Oracle Data Miner, go to Properties for the node where the model is built.
Models created using Oracle Data Miner may contain BALANCED, NATURAL, CUSTOM, or TUNED. Oracle Data Miner inserts these values to indicate if the model has been tuned and in what way it was tuned.

Input (NB)

The Input tab for Naive Bayes is displayed for models that can be scored only.

A list of the attributes used to build the model. For each attribute the following information is displayed:

- Name: The name of the attribute.

- Data Type: The data type of the attribute.

- Mining Type: Categorical or Numerical.

- Target: The icon indicates that the attribute is a target attribute.

- Data Preparation: YES indicates that data preparation was performed. It helps to distinguish between User and Auto Data Preparation (ADP) as ADP could be turned off, but the user could still embedded a transformation. If Data Preparation is indicated as YES, then select the column and click it. Each group can contain an Input and a Reverse Expression. If there is no Reverse Expression, then it is not displayed. If there is no Input, then nothing is displayed. Transformations are displayed in SQL notation.

- Partition Key: YES indicates that the attribute is a partition key.

Partition Keys

The Partition Keys tab lists the columns that are partitioned.

Along with the partitioned columns, the Partition Keys tab lists the following details:

- Partition Name

- Source

- Data Type

- Value

Weights

The Weights tab displays the weights that are calculated by the system for each target value.

If you tune the model, then the weights may change.

Target Values

The Target Values tab displays the target attributes, their data types, and the values of each target attribute.

The Target Values tab for Naive Bayes displays the following:

- Target Attributes

- Data Types

- Values of each target attributes

Nonnegative Matrix Factorization

Nonnegative Matrix Factorization (NMF) is the unsupervised algorithm used by Oracle Data Mining for feature extraction.

- To build an NMF model, use a Feature Extraction node.

- To apply an NMF model to new data, use an Apply node.

- Using Nonnegative Matrix Factorization
Nonnegative Matrix Factorization (NMF) is useful when there are many attributes and the attributes are ambiguous or have weak predictability.

- How Does Nonnegative Matrix Factorization Work
Non-Negative Matrix Factorization (NMF) uses techniques from multivariate analysis and linear algebra.

- NMF Algorithm Settings
Lists the settings supported by the Nonnegative Matrix Factorization (NMF) algorithm.

- NMF Model Viewer
In the NMF model viewer, you can view information related to the model and algorithm, such as coefficients and settings.

Related Topics

- Apply Node

- Feature Extraction Node

Using Nonnegative Matrix Factorization

Nonnegative Matrix Factorization (NMF) is useful when there are many attributes and the attributes are ambiguous or have weak predictability.

By combining attributes, NMF can produce meaningful patterns, topics, or themes.

NMF is especially well-suited for text mining. In a text document, the same word can occur in different places with different meanings. For example, hike can be applied to the outdoors or to interest rates. By combining attributes, NMF introduces context, which is essential for predictive power:

- "hike" + "mountain" -> "outdoor sports"

- "hike" + "interest" -> "interest rates"

How Does Nonnegative Matrix Factorization Work

Non-Negative Matrix Factorization (NMF) uses techniques from multivariate analysis and linear algebra.

NMF decomposes multivariate data by creating a user-defined number of features. Each feature is a linear combination of the original attribute set. The coefficients of these linear combinations are nonnegative.

NMF decomposes a data matrix V into the product of two lower rank matrices W and H so that V is approximately equal to W times H. NMF uses an iterative procedure to modify the initial values of W and H so that the product approaches V. The procedure terminates when the approximation error converges or the specified number of iterations is reached.

When applying to a model, an NMF model maps the original data into the new set of attributes (features) discovered by the model.

NMF Algorithm Settings

Lists the settings supported by the Nonnegative Matrix Factorization (NMF) algorithm.

The settings are:

- Convergence Tolerance: Indicates the minimum convergence tolerance value. The default is 0.5.

- Automatic Data Preparation: ON (Default). Indicates automatic data preparation.

- Non Negative Scoring: Controls if NMF scoring results are truncated at zero, that is, no negative values are produced. Options are Enabled or Disabled. By default, non negative scoring is enabled.

- Number of Features:The default is to not specify the number of features. If you do not specify the number of features, then the algorithm determines the number of features.
To specify the number of features, then select Specify number of features and enter the integer number of features. The number of features must be a positive integer less than or equal to the minimum of the number of attributes and to the number of cases. In many cases, 5 or some other number less than or equal to 7 gives good results.

- Number of iterations: Indicates the maximum number of iterations to be performed. The default is 50.

- Random Seed: It is the random seed for the sample. The default value is -1. The seed can be changed. If you plan to repeat this operation to get the same results, then ensure that you use the same random seed.

NMF Model Viewer

In the NMF model viewer, you can view information related to the model and algorithm, such as coefficients and settings.

The NMF Model Viewer has these tabs:

- Coefficients (NMF)
For a given Feature ID, the coefficients are displayed in the Coefficients grid.

- Features
The Features tab displays all the features along with the Feature IDs and the corresponding items.

- Settings (NMF)
The Settings tab contains information related to inputs, build details, algorithm settings, and other general settings.

Related Topics

- Viewing Models in Model Viewer

Coefficients (NMF)

For a given Feature ID, the coefficients are displayed in the Coefficients grid.

The title of the grid Coefficient x of y displays the number of rows returned out of all the rows available in the model.

By default, Feature IDs are integers.

Fetch Size limits the number of rows returned. The default is 1000 or the value specified in the Preference settings for Model Viewers.

You can perform the following tasks:

- Rename

- Filter

The Coefficients grid has these columns:

- Attribute: Attribute name

- Value: Value of attribute

- Coefficient: The value is shown as a bar with the value centered in the bar. Positive values are light blue. Negative values are red.

- Rename (NMF)
You can rename any Feature ID in the Rename dialog box.

- Filter (NMF)
You can create filters, and view filter according to different categories such as attributes, values and coefficients in the Filter dialog box.

Rename (NMF)

You can rename any Feature ID in the Rename dialog box.

To rename the selected Feature ID:

1. Enter in the new name in the Feature ID field.

2. Click OK.

Note:

Different features should have different names.

Filter (NMF)

You can create filters, and view filter according to different categories such as attributes, values and coefficients in the Filter dialog box.

To view the filter categories, click .

The filter categories are

- Attribute (Default): Search for an attribute name.

- Value: This is the value column.

- Coefficient: This is the coefficient column.

To create a filter, enter a string in the text box. After a string has been entered, icon is displayed. To clear the filter, click the icon.

Features

The Features tab displays all the features along with the Feature IDs and the corresponding items.

The lower panel contains the following tabs:

- Tag Cloud: Displays the selected feature in a tag cloud format. You can sort the feature tags based on coefficients or alphabetical order. You can also view them in ascending or descending order. To copy and save the cloud image, right-click and select:

- Save Image As

- Copy Image to Clipboard

- Coefficients: Displays the attribute of the selected feature along with their values and coefficients in a tabular format.

Settings (NMF)

The Settings tab contains information related to inputs, build details, algorithm settings, and other general settings.

- Summary (NMF)
The Summary tab contains information related to build details, algorithm settings, and other general settings.

- Inputs (NMF)
The Inputs tab displays the list of attributes that are used to build the model.

Summary (NMF)

The Summary tab contains information related to build details, algorithm settings, and other general settings.

The sections in the Summary tab are:

- General settings lists the following:

- Type of model (Classification, Regression, and so on)

- Owner of the model (the Schema where the model was built)

- Model Name

- Creation Date

- Duration of the model build (in minutes)

- Size of the model (in MB)

- Comments

- Algorithm Settings lists the following:

- The name of the algorithm used to build the model.

- The algorithm settings that control the model build.

- Build Details displays computed settings. Computed settings are generated by Oracle Data Mining when the model is created.

Related Topics

- General Settings

- NMF Algorithm Settings

Inputs (NMF)

The Inputs tab displays the list of attributes that are used to build the model.

Oracle Data Miner does not necessarily use all of the attributes in the build data. For example, if the values of an attribute are constant, then that attribute is not used.

For each attribute used to build the model, this tab displays:

- Name: The name of the attribute.

- Data Type: The data type of the attribute

- Mining Type: Categorical or Numerical

- Data Preparation: YES indicates that data preparation was performed. It helps to distinguish between User and Auto Data Preparation (ADP) as ADP could be turned off, but the user could still embedded a transformation. If Data Preparation is indicated as YES, then select the column and click it. Each group can contain an Input and a Reverse Expression. If there is no Reverse Expression, then it is not displayed. If there is no Input, then nothing is displayed. Transformations are displayed in SQL notation.

- Partition Key: YES indicates that the attribute is a partition key.

Orthogonal Partitioning Clustering

Orthogonal Partitioning Clustering is a clustering algorithm that is proprietary to Oracle.

(O-Cluster) The requirements to build and apply the O-Cluster algorithm:

- To build OC models, use a Clustering node.

- To apply an OC model to new data, use an Apply node.

The following topics describe an O-Cluster:

- O-Cluster Algorithm
The O-Cluster (OC) algorithm creates a hierarchical grid-based clustering model. That is, it creates axis-parallel (orthogonal) partitions in the input attribute space.

- OC Algorithm Settings
Lists the settings supported by O-Cluster (OC) algorithm.

- OC Model Viewer
In the OC Model Viewer, you can examine the details of an OC model.

- Interpreting Cluster Rules
Cluster Rules are presented as mathematical notations.

Related Topics

- Apply Node

- Clustering Node

O-Cluster Algorithm

The O-Cluster (OC) algorithm creates a hierarchical grid-based clustering model. That is, it creates axis-parallel (orthogonal) partitions in the input attribute space.

The algorithm operates recursively. The resulting hierarchical structure represents an irregular grid that tessellates the attribute space into clusters. The resulting clusters define dense areas in the attribute space.

The clusters are described by intervals along the attribute axes and the corresponding centroids and histograms. The sensitivity parameter defines a baseline density level. Only areas with a peak density above this baseline level can be identified as clusters.

The clusters discovered by O-Cluster are used to generate a Bayesian probability model that is then used during scoring (model apply) for assigning data points to clusters. The generated probability model is a mixture model where the mixture components are represented by a product of independent normal distributions for numerical attributes and multinomial distributions for categorical attributes.

O-Cluster goes through the data in chunks until it converges. There is no explicit limit on the number of rows processed.

O-Cluster handles missing values naturally as missing at random. The algorithm does not support nested tables and thus does not support sparse data.

Note:

OC does not support text.

OC Algorithm Settings

Lists the settings supported by O-Cluster (OC) algorithm.

The settings are:

- Number of Clusters: It is the maximum number of leaf clusters generated by the algorithm. The default is 10.

- Buffer Size: It is the maximum size of the memory buffer, in logical records, that can be used by the algorithm. The default is 50,000 logical records.

- Sensitivity: It is a number between 0 (fewer clusters) and 1 (more clusters). The default is 0.5. This value specifies the peak density required for separating a new cluster. This value is related to the global uniform density.

OC Model Viewer

In the OC Model Viewer, you can examine the details of an OC model.

The OC Model viewer has these tabs:

- EM, KM, and OC Tree Viewer
The Tree Viewer is the graphical tree for hierarchical clusters.

- Cluster (Viewer)
The Cluster tab enables you to view information about a selected cluster. The viewer supports filtering so that only selected probabilities are displayed.

- EM, KM, and OC Compare
In the Compare tab, you can compare two clusters in the same model.

- Detail (OC)
In the Details tab, you can view details of a cluster. You can discover what values of an attribute are in the selected cluster.

- Settings (OC)
The Settings tab displays information about how the model was built:

Related Topics

- Viewing Models in Model Viewer

- Interpreting Cluster Rules

EM, KM, and OC Tree Viewer

The Tree Viewer is the graphical tree for hierarchical clusters.

The tree viewer for Expectation Maximization, k-Means, and Orthogonal Clustering operate in the same way. When you view the tree:

- The Workflow Thumbnail opens to give you a view of the entire tree.

- The Structure window helps you navigate and analyze the tree.

You can compare the attributes in a given node with the attributes in the population using EM, KM, and OC Compare.

To view information about a particular node:

1. Select the node.

2. In the lower pane, the following are displayed in each of these tabs:

- Centroid: Displays the centroid of the cluster

- Cluster Rule: Displays the rule that all elements of the cluster satisfy.

Display Control:

The following control the display of the tree as a whole:

- Zoom in: Zooms in to the diagram, providing a more detailed view of the rule.

- Zoom out: Zooms out from the diagram, providing a view of much or all of the rule.

- Percent size: Enables you select an exact percentage to zoom the view.

- Fit to Window: Zooms out from the diagram until the whole diagram fits within the screen.

- Layout Type: Enables you to select horizontal layout or vertical layout; the default is vertical.

- Expand: All nodes shows branches of the tree.

- Show more detail: Shows more data for each tree node. Click again to show less detail.

- Top Attributes: Displays the top N attributes. N is 5 by default. To change N, select a different number from the list.

- Refresh: Enables you to apply the changed Query Settings.

- Query Settings: Enables you to change the number of top settings. The default is 10. You can save a different number as the new default.

- Save Rules

Related Topics

- Save Rules

- Structure Window

- EM, KM, and OC Compare

- Workflow Thumbnail

Cluster (Viewer)

The Cluster tab enables you to view information about a selected cluster. The viewer supports filtering so that only selected probabilities are displayed.

The Cluster tab for EM, KM, and OC operate in the same way.

The following information is displayed:

- Cluster: The ID of the cluster being viewed. To view another cluster, select a different ID from the menu. You can view Leaves Only (terminal clusters) by selecting Leaves Only. Leaves Only is the default.

- Fetch Size: Default is 20. You can change this value.
If you change Fetch Size, then click Query to see the new display.

The grid lists the attributes in the cluster. For each attribute, the following information is displayed:

- Name of the attribute.

- Histogram of the attribute values in the cluster.

- Confidence displayed as both a number and with a bar indicating a percentage. If confidence is very small, then no bar is displayed.

- Support, the number of cases.

- Mean, displayed for numeric attributes.

- Mode, displayed for categorical attributes.

- Variance

To view a larger version of the histogram, select an attribute; the histogram is displayed below the grid. Place the cursor over a bar in the histogram to see the details of the histogram including the exact value.

You can search the attribute list for a specific attribute name or for a specific value of mode. To search, use the search box.

The drop-down list enables you to search the grid by Attribute (the default) or by Mode. Type the search term in the box next to .

To clear a search, click .

Other Tabs: The NB Model Viewer also has these tabs:

- EM, KM, and OC Tree Viewer

- EM, KM, and OC Compare

- Settings

Related Topics

- EM, KM, and OC Tree Viewer

- EM, KM, and OC Compare

- KM Settings

EM, KM, and OC Compare

In the Compare tab, you can compare two clusters in the same model.

The Compare tab for EM, KM, and OC operate in the same way. The display enables you to select the two clusters to compare.

You can perform the following tasks:

- Compare Clusters: To select clusters to compare, pick them from the lists. The clusters are compared by comparing attribute values. The comparison is displayed in a grid. You can use Compare to compare an individual cluster with the population.

- Rename Clusters: To rename clusters, click Edit. This opens the Rename Cluster dialog box. By default, only Show Leaves is displayed. To show all nodes, then deselect Leaves Only. The default Fetch Size is 20. You can change this value.

- Search Attribute: To search an attribute, enter its name in the search box. You can also search by rank.

- Create Query: If you make any changes, then click Query.

For each cluster, a histogram is generated that shows the attribute values in the cluster. To see enlarged histograms for a cluster, click the attribute that you are interested in. The enlarged histograms are displayed below the attribute grid.

In some cases, there may be missing histograms in a cluster.

Detail (OC)

In the Details tab, you can view details of a cluster. You can discover what values of an attribute are in the selected cluster.

The viewer supports filtering so that only selected probabilities are displayed. The following information is displayed:

- Cluster: The ID of the cluster being viewed. You can change the cluster by selecting a different ID. Select Leaves Only to view terminal clusters only.

- Fetch Size: The number of columns selected. The default is 50. You can change the Fetch Size. If you change the Fetch Size, then click Query.

The grid lists the attributes in the cluster. For each attribute, the following information is displayed:

- Attribute: An attribute is a predictor in a predictive model or an item of descriptive information in a descriptive model. Data attributes are the columns of data that are used to build a model. Data attributes undergo transformations so that they can be used as categoricals or numericals by the model. Categoricals and numericals are model attributes.

- Histogram: The attribute values in the selected cluster are displayed as a histogram.
To view a larger version of the histogram, select an attribute. The histogram is displayed below the grid. Place the cursor over a bar in the histogram to see the details of the histogram including the exact value.

- Confidence: Displayed as both a number and with a bar indicating a percentage. If the confidence is very small, then no bar is displayed.

- Support: The number of cases.

- Mean: Displayed for numeric attributes.

- Mode: Displayed for categorical attributes.

- Variance

You can perform the following tasks:

- Sort the attributes in the cluster. To sort, click the appropriate column heading in the grid. For example, to sort by attribute name, click Attribute. The attributes are sorted by:

- Confidence

- Support

- Mean

- Mode

- Variance

- Attribute name

- Search the attribute list for a specific attribute name or for a specific value of mode. To search, use the search box next to .

- Search the grid by Attribute. The drop down list enables you to search the grid by Attribute (the default) or by Mode. Enter the search term in the search field. To clear a search, click .

Other Tabs: The OC Model Viewer has the Settings tab.

Related Topics

- Settings (OC)

Settings (OC)

The Settings tab displays information about how the model was built:

- Summary (OC)
The Summary tab contains information related to build details, algorithm settings, and other general settings.

- Inputs (OC)
The Inputs tab is displayed for models that can be scored only.

Related Topics

- Detail (OC)

Summary (OC)

The Summary tab contains information related to build details, algorithm settings, and other general settings.

The sections in the Summary tab are:

- General settings lists the following:

- Type of model (Classification, Regression, and so on)

- Owner of the model (the Schema where the model was built)

- Model Name

- Creation Date

- Duration of the model build (in minutes)

- Size of the model (in MB)

- Comments

- Algorithm Settings settings lists the following:

- The name of the algorithm.

- The settings that control the model build. Algorithm settings are specified when the build node is defined.

- Build Details displays computed settings. Computed settings are generated by Oracle Data Mining when the model is created.

Related Topics

- General Settings

- OC Algorithm Settings

Inputs (OC)

The Inputs tab is displayed for models that can be scored only.

It displays the following information:

- Name: The name of the attribute.

- Data Type: The data type of the attribute

- Mining Type: Categorical or Numerical

- Data Preparation: YES indicates that data preparation was performed. It helps to distinguish between User and Auto Data Preparation (ADP) as ADP could be turned off, but the user could still embedded a transformation. If Data Preparation is indicated as YES, then select the column and click it. Each group can contain an Input and a Reverse Expression. If there is no Reverse Expression, then it is not displayed. If there is no Input, then nothing is displayed. Transformations are displayed in SQL notation.

- Partition Key: YES indicates that the attribute is a partition key.

Interpreting Cluster Rules

Cluster Rules are presented as mathematical notations.

After running the Clustering Build node, the Clustering node builds three models, each using O-Cluster, k-Means, and Expectation Maximization algorithm. In the Model viewer, the Clustering rules of each cluster are displayed in the Rules tab in the lower pane of the Tree tab. For each rule, you can view the details of the algorithm in the Settings tab under Summary. Select a cluster in the tree node to view the rules of the selected cluster.

Example 13-1 Example of a Cluster Rule

Suppose the rules of the selected cluster is as follows:

If TIME_AS_CUSTOMER In ("1", "2")
And N_OF_DEPENDENTS = "(.857143; 1.71429]"

And HOUSE_OWNERSHIP = "1"

And N_MORTGAGES = "1"

And REGION In ("NorthEast", "South")

Then Cluster is: 19

The rule is generated in terms of bins. The rule in this example can be interpreted as follows:

In the rule If TIME_AS_CUSTOMER In ("1", "2"), the attribute TIME_AS_CUSTOMER considers rows that have the value of 1 and 2. Since the column's mining type is categorical the rule is expressed as a set.

The rule N_OF_DEPENDENTS = "(.857143; 1.71429]"means .857143 < N_OF_DEPENDENTS <= 1.71429. Since the column's mining type is numerical the bin is expressed as a range.

The rules HOUSE_OWNERSHIP = "1" and N_MORTGAGES = "1" means that the attributes HOUSE_OWNERSHIP and N_MORTGAGES consider rows that have value 1.

The rule REGION In ("NorthEast", "South") means that the attribute REGION considers rows that contain the values “Northeast” and “South” in it.

Based on the rules, the cluster is derived to be 19.

Singular Value Decomposition and Principal Components Analysis

Singular Value Decomposition (SVD) and Principal Component Analysis (PCA) are unsupervised algorithms used by Oracle Data Mining for feature extraction.

Unlike NMF, SVD and PCA are orthogonal linear transformations that are optimal for capturing the underlying variance of the data. This property is extremely useful for reducing the dimensionality of high-dimensional data and for supporting meaningful data visualization.

Note:

Singular Value Decomposition (SVD) and Principal Component Analysis (PCA) require Oracle Database 12c or later.

In addition to dimensionality reduction, SVD and PCA have several other important applications, such as, data denoising (smoothing), data compression, matrix inversion, and solving a system of linear equations. All these areas can be effectively supported by the Oracle Data Mining implementation SVD/PCA.

SVD is implemented as a feature extraction algorithm. PCA is implemented as a special scoring method for the SVD algorithm.

- Build and Apply SVD and PCA Models
To build an SVD or PCA model, use a Feature Extraction node.

- PCA Algorithm Settings
Lists the settings supported by PCA algorithm.

- PCA Model Viewer
You can you examine the details of a Principal Components Analysis model in the PCA Model Viewer.

- SVD Algorithm Settings
Lists the settings supported by SVD algorithm.

- SVD Model Viewer
You can examine the details of a Singular Value Decomposition model in the SVD Model Viewer.

Related Topics

- Nonnegative Matrix Factorization

- Build and Apply SVD and PCA Models

Build and Apply SVD and PCA Models

To build an SVD or PCA model, use a Feature Extraction node.

A Feature Extraction model creates a Feature Build Node. If you are connected to Oracle Database 12c or later, then a Feature Build node creates one NMF model and one PCA model. You can add an SVD model.

To apply an SVD or PCA model, use an Apply node.

Related Topics

- Apply Node

- Feature Extraction Node

PCA Algorithm Settings

Lists the settings supported by PCA algorithm.

- Number of features: The default is System Determined. To specify a value, select User specified and enter in an integer value.

- Solver: The solver setting indicates the type of SVD solver used for computing the Principal Components Analysis (PCA) for the data. Solvers are grouped into narrow data solvers or Tall-Skinny SVD solvers and wide data solvers or Stochastic SVD solvers. The options are:

- Tall-Skinny (for QR computation). This is the default solver for narrow data.

- Tall-Skinny (for Eigenvalue computation)

- Stochastic (for QR computation). If you select this option, then click Option. This opens the Solver (Stochastic QR Computation) dialog box. This is the default for wide data.

- Stochastic (for Eigenvalue computation)

Note:

The solvers using QR computation (tssvd and ssvd) are more stable and produce higher quality results for ill-conditioned data matrix than the solver using Eigenvalue computation (tseigen and steigen). The improved stability comes at a higher computation cost.

- Tolerance: By default, it is set to System Determined. To specify a value, click User Specified. The value must be a number greater than 0 and less than 1.

- Approximate Computation:The default is System Determined. You can select either Enable or Disable. Approximate computations improve performance.

- Projections:The default is to not select Projections.

- Number of Features: The default is System Determined. You can specify a number.

- Scoring Mode: It is the scoring mode to use, either Singular Value Decomposition Scoring or Principal Components Analysis Scoring. The default is Principal Components Analysis Scoring (PCA scoring).

- When the build data is scored with SVD, the projections will be the same as the U matrix.

- When the build data is scored with PCA, the projections will be the product of the U and S matrices.

- U Matrix Output: Whether or not the U matrix produced by SVD persists. The U matrix in SVD has as many rows as the number of rows in the Build data. To avoid creating a large model, the U matrix persists only when U Matrix Output is enabled. When U Matrix Output is enabled, the Build data must include a Case ID. The default is Disable.

- Solver (Stochastic QR Computation)

Related Topics

- SVD Algorithm Settings

Solver (Stochastic QR Computation)

You can specify the settings for the Stochastic (QR computation) solver here:

1. In the Oversampling field, specify a value greater than or equal to 1 and less than or equal to 10000. Default is 5. A larger oversampling value yields better accuracy, but incurs longer training cost. The value configures the number of columns in the sampling matrix used by the Stochastic SVD solver. The number of columns in this matrix is equal to the requested number of features and the oversampling setting.

2. In the Power Iterations field, specify a value greater than or equal to 0 and less than or equal to 20. Default is 2. The value improves the accuracy of the solver.

3. In the Random Seed field, specify a value greater than or equal to 0 and less than or equal to 4294967296. Default is 0. The random seed value initializes the sampling matrix used by the Stochastic SVD solver.

4. Click OK.

PCA Model Viewer

You can you examine the details of a Principal Components Analysis model in the PCA Model Viewer.

The model viewer has these tabs:

- Coefficients (PCA)
For a given Feature ID, the coefficients are displayed in the Coefficients grid.

- PCA Scree Plot
A scree plot displays the eigenvalues associated with a component or factor.

- Features
The Features tab displays all the features along with the Feature IDs and the corresponding items.

- PCA Details
The Details tab displays the value for the global details of the SVD model.

- Settings (PCA)
The Settings tab displays information about how the model was built.

Related Topics

- Viewing Models in Model Viewer

Coefficients (PCA)

For a given Feature ID, the coefficients are displayed in the Coefficients grid.

The title of the grid Coefficients x of y displays number of rows returned out of all the rows available in the model. By default, Feature IDs are integers (1, 2, 3, …). The Eigenvalue for the selected Feature ID is displayed as a read-only value.

You can perform the following tasks:

- View and create filter categories

- Rename feature IDs

The Coefficients grid has these columns:

- Attribute

- Singular Value
The value is shown as a bar with the value centered in the bar. Positive values are light blue; negative values are red.

The default is Sort by absolute value. If you deselect this option, then click Query.

- Rename (PCA)

- Filter (PCA)

Rename (PCA)

In the Rename dialog box, you can rename the selected Feature ID. To rename:

1. Enter in the new name in the Feature ID field.

2. Click OK.

Note:

Different features should have different names.

Filter (PCA)

To view the filter categories, click .

The filter categories are:

- Attribute: (Default). Search for an attribute name.

- Singular Value: The Singular value column

To create a filter, enter a string in the text box. After a string has been entered, is displayed. To clear the filter, click it.

PCA Scree Plot

A scree plot displays the eigenvalues associated with a component or factor.

You can use scree plots in Principal Components Analysis to visually assess which components or factors explain most of the variability in the data. In the PCA Scree Plot:

- Features are plotted along the X-axis.

- Cutoff is plotted along the Y-axis.

- Variance is plotted as a red line.

- Cumulative percent is plotted as a blue line.

A grid below the graph shows Eigenvalue, Variance, and Cumulative Percent Variance for each Feature ID.

Features

The Features tab displays all the features along with the Feature IDs and the corresponding items.

The lower panel contains the following tabs:

- Tag Cloud: Displays the selected feature in a tag cloud format. You can sort the feature tags based on coefficients or alphabetical order. You can also view them in ascending or descending order. To copy and save the cloud image, right-click and select:

- Save Image As

- Copy Image to Clipboard

- Coefficients: Displays the attribute of the selected feature along with their values and coefficients in a tabular format.

PCA Details

The Details tab displays the value for the global details of the SVD model.

The following information is displayed:

- Number of Components

- Suggested Cutoff

Settings (PCA)

The Settings tab displays information about how the model was built.

The Settings tab contains these tabs:

- Summary (PCA)
The Summary tab contains information related to build details, algorithm settings, and other general settings.

- Inputs (PCA)
The Inputs tab displays the list of attributes that are used to build the model.

Summary (PCA)

The Summary tab contains information related to build details, algorithm settings, and other general settings.

The sections in the Summary tab contains the following information:

- General settings lists the following:

- Type of model (Classification, Regression, and so on

- Owner of the model (the schema where the model was built)

- Model Name

- Creation Date

- Duration of the model build (in minutes)

- Size of the model (in MB)

- Comments

- Algorithm Settings settings lists the following:

- The name of the algorithm used to build the model.

- The algorithm settings that control the model build.

- Build Details displays computed settings. Computed settings are generated by Oracle Data Mining when the model is created.

Related Topics

- General Settings

- PCA Algorithm Settings

Inputs (PCA)

The Inputs tab displays the list of attributes that are used to build the model.

Oracle Data Miner does not necessarily use all of the attributes in the build data. For example, if the values of an attribute are constant, then that attribute is not used.

For each attribute used to build the model, this tab displays:

- Name: The name of the attribute.

- Data Type: The data type of the attribute.

- Mining Type: Categorical or Numerical.

- Data Preparation: YES indicates that data preparation was performed. It helps to distinguish between User and Auto Data Preparation (ADP) as ADP could be turned off, but the user could still embedded a transformation. If Data Preparation is indicated as YES, then select the column and click it. Each group can contain an Input and a Reverse Expression. If there is no Reverse Expression, then it is not displayed. If there is no Input, then nothing is displayed. Transformations are displayed in SQL notation.

- Partition Key: YES indicates that the attribute is a partition key.

SVD Algorithm Settings

Lists the settings supported by SVD algorithm.

- Approximate Computation: Specifies whether the algorithm should use approximate computations to improve performance. For SVD, approximation is often appropriate for data sets with many columns. An approximate low-rank decomposition provides good solutions at a reasonable computational cost. If you disable approximate computations for SVD, then approximation depends on the characteristics of the data. For data sets with more than 2500 attributes (the maximum number of features allowed) only approximate decomposition is possible. If approximate computation is disabled for a data set with more than 2500 attributes, then an exception is raised.
Values for Approximate Computation are:

- System Determined (Default)

- Enable

- Disable

- Automatic Preparation: ON or OFF. The default is ON.

- Number of Features: System Determined (Default). You can specify a number.

- Solver: The setting indicates the solver to be used for computing the Singular Value Decomposition (SVD) of the data. Solvers are grouped into narrow data solvers or Tall-Skinny SVD solvers and wide data solvers or Stochastic SVD solvers. The options are:

- Tall-Skinny (for QR computation). This is the default solver for narrow data.

- Tall-Skinny (for Eigenvalue computation)

- Stochastic (for QR computation). If you select this option, then click Option. This opens the Solver (Stochastic QR Computation) dialog box. This is the default for wide data.

- Stochastic (for Eigenvalue computation)

Note:

The solvers using QR computation (tssvd and ssvd) are more stable and produce higher quality results for ill-conditioned data matrix than the solver using Eigenvalue computation (tseigen and steigen). The improved stability comes at a higher computation cost.

- Tolerance: By default, it is set to System Determined. To specify a value, click User Specified. The value must be a number greater than 0 and less than 1.

- Scoring Mode: It is the scoring mode to use, either Singular Value Decomposition Scoring or Principal Components Analysis Scoring. The default is Singular Value Decomposition Scoring (SVD scoring).

- When the build data is scored with SVD, the projections will be the same as the U matrix.

- When the build data is scored with PCA, the projections will be the product of the U and S matrices.

- U Matrix Output: Whether or not the U matrix produced by SVD persists. The U matrix in SVD has as many rows as the number of rows in the build data. To avoid creating a large model, the U matrix persists only when U Matrix Output is enabled. When U Matrix Output is enabled, the build data must include a Case ID. The default is Disable.

Related Topics

- Solver (Stochastic QR Computation)

SVD Model Viewer

You can examine the details of a Singular Value Decomposition model in the SVD Model Viewer.

The SVD model viewer has these tabs:

- Coefficients (SVD)
For a given Feature ID, the coefficients are displayed in the Coefficients grid.

- Features
The Features tab displays all the features along with the Feature IDs and the corresponding items.

- SVD Singular Values

- SVD Details

- Settings (SVD)
The Settings tab displays information about how the model was built.

Related Topics

- Viewing Models in Model Viewer

Coefficients (SVD)

For a given Feature ID, the coefficients are displayed in the Coefficients grid.

The title of the grid Coefficients x of y displays the number of rows returned out of all the rows available in the model. By default, Feature IDs are integers.

The Eigenvalue for the selected Feature ID is displayed as a read-only value.

Fetch Size limits the number of rows returned. The default is 1,000 or the value specified in the Preference settings for Model Viewers.

You can perform the following tasks:

- Rename

- Filter

The Coefficients grid has these columns:

- Attribute

- Singular Value
The value is shown as a bar with the value centered in the bar. Positive values are light blue; negative values are red.

The default is Sort by absolute value. To sort by signed value, deselect the option and then click Query.

- Rename (SVD)

- Filter (SVD)

Rename (SVD)

You can rename the selected Feature ID. Enter in the new name and click OK. Different features should have different names.

Filter (SVD)

To view the filter categories, click .

The filter categories are:

- Attribute, the default; search for an attribute name

- Singular Value, the singular value column

To create a filter, enter a string in the text box. After a string has been entered, is displayed. To clear the filter, click it.

Features

The Features tab displays all the features along with the Feature IDs and the corresponding items.

The lower panel contains the following tabs:

- Tag Cloud: Displays the selected feature in a tag cloud format. You can sort the feature tags based on coefficients or alphabetical order. You can also view them in ascending or descending order. To copy and save the cloud image, right-click and select:

- Save Image As

- Copy Image to Clipboard

- Coefficients: Displays the attribute of the selected feature along with their values and coefficients in a tabular format.

SVD Singular Values

The Singular Values for each Feature ID are displayed in a grid.

SVD Details

This tab displays the value for these global details of the SVD model:

- Number of Components

- Suggested Cutoff

Settings (SVD)

The Settings tab displays information about how the model was built.

The Settings tab contains these tabs:

- Summary

- Inputs

- Summary (SVD)
The Summary tab contains information related to build details, algorithm settings, and other general settings.

- Inputs (SVD)
The Inputs tab displays the list of attributes that are used to build the model.

Summary (SVD)

The Summary tab contains information related to build details, algorithm settings, and other general settings.

The sections in the Summary tab contains the following information:

- General settings list the following:

- Type of model (Classification, Regression, and so on)

- Owner of the model (the Schema where the model was built)

- Model Name

- Creation Date

- Duration of the model build (in minutes)

- Size of the model (in MB)

- Comments

- Algorithm Settings list the following:

- The name of the algorithm used to build the model.

- The algorithm settings that control model build.

- Build Details displays computed settings. Computed settings are generated by Oracle Data Mining when the model is created.

Related Topics

- General Settings

- SVD Algorithm Settings

Inputs (SVD)

The Inputs tab displays the list of attributes that are used to build the model.

Oracle Data Miner does not necessarily use all of the attributes in the build data. For example, if the values of an attribute are constant, then that attribute is not used.

For each attribute used to build the model, this tab displays:

- Name: The name of the attribute.

- Data Type: The data type of the attribute.

- Mining Type: Categorical or Numerical.

- Data Preparation: YES indicates that data preparation was performed. It helps to distinguish between User and Auto Data Preparation (ADP) as ADP could be turned off, but the user could still embedded a transformation. If Data Preparation is indicated as YES, then select the column and click it. Each group can contain an Input and a Reverse Expression. If there is no Reverse Expression, then it is not displayed. If there is no Input, then nothing is displayed. Transformations are displayed in SQL notation.

- Partition Key: YES indicates that the attribute is a partition key.

Support Vector Machine

You can use the Support Vector Machine (SVM) algorithm to build Classification, Regression, and Anomaly Detection models.

The following topics explain Support Vector Machine:

- Support Vector Machine Algorithms
The Support Vector Machines (SVM) algorithms are a suite of algorithms that can be used with variety of problems and data. By changing one kernel for another, SVM can solve a variety of data mining problems.

- Building and Testing SVM Models
You specify building a model by connecting the Data Source node that represents the build data to an appropriate Build node.

- Applying SVM Models
You apply a model to new data to predict behavior.

- SVM Classification Algorithm Settings
The settings that you can specify for the Support Vector Machine (SVM) algorithm depend on the Kernel function that you select.

- SVM Classification Test Viewer
By default, any Classification or Regression model is automatically tested. You have the option to view the test results.

- SVM Classification Model Viewer
You can examine the details of a SVM Classification model in the SVM Model Viewer.

- SVM Regression Algorithm Settings
The settings that you can specify for the Support Vector Machine (SVM) algorithm depend on the Kernel function that you select.

- SVM Regression Test Viewer
By default, any Classification or Regression model is automatically tested. You have the option to view the test results.

- SVM Regression Model Viewer
You can examine a SVM (Regression) Model in the SVM Regression Model Viewer.

Support Vector Machine Algorithms

The Support Vector Machines (SVM) algorithms are a suite of algorithms that can be used with variety of problems and data. By changing one kernel for another, SVM can solve a variety of data mining problems.

Oracle Data Mining supports two kernel functions:

- Linear

- Gaussian

The key features of SVM are:

- SVM can emulate traditional methods, such as Linear Regression and Neural Nets, but goes far beyond those methods in flexibility, scalability, and speed.

- SVM can be used to solve the following kinds of problems: Classification, Regression, and Anomaly Detection.
Oracle Data Mining uses SVM as the one-class classifier for anomaly detection. When SVM is used for anomaly detection, it has the classification mining function but no target. Applying a One-class SVM model results in a prediction and a probability for each case in the scoring data. If the prediction is 1, then the case is considered typical. If the prediction is 0, then the case is considered anomalous.

- How Support Vector Machines Work
SVM solves regression problems by defining an n-dimensional tube around the data points, determining the vectors giving the widest separation.

- SVM Kernel Functions
The Support Vector Machine (SVM) algorithm supports two kernel functions: Gaussian and Linear.

How Support Vector Machines Work

SVM solves regression problems by defining an n-dimensional tube around the data points, determining the vectors giving the widest separation.

Data records with n attributes can be considered as points in n-dimensional space. SVM attempts to separate the points into subsets with homogeneous target values. Points are separated by hyperplanes in the linear case, and by non-linear separators in the non-linear case (Gaussian). SVM finds those vectors that define the separators giving the widest separation of classes (the support vectors). This is easy to visualize if n = 2; in that case, SVM finds a straight line (linear) or a curve (non-linear) separating the classes of points in the plane.

SVM Kernel Functions

The Support Vector Machine (SVM) algorithm supports two kernel functions: Gaussian and Linear.

The choice of kernel function depends on the type of model (classification or regression) that you are building and on your data.

When you choose a Kernel function, select one of the following:

- System Determined (Default)

- Gaussian

- Linear

For Classification models and Anomaly Detection models, use the Gaussian kernel for solving problems where the classes are not linearly separable, that is, the classes cannot be separated by lines or planes. Gaussian kernel models allow for powerful non-linear class separation modeling. If the classes are linearly separable, then use the linear kernel.

For Regression problems, the linear kernel is similar to approximating the data with a line. The linear kernel is more robust than fitting a line to the data. The Gaussian kernel approximates the data with a non-linear function.

Building and Testing SVM Models

You specify building a model by connecting the Data Source node that represents the build data to an appropriate Build node.

By default, a Classification or Regression node tests all the models that it builds. By default, the test data is created by splitting the input data into build and test subsets. Alternatively, you can connect two data sources to the build node, or you can test the model using a Test node.

You can build three kinds of SVM models:

- SVM Classification Models
SVM Classification (SVMC) is based on the concept of decision planes that define decision boundaries.

- SVM Regression Models
SVM Regression (SVMR) models tries to find a continuous function such that the maximum number of data points lie within the epsilon-wide insensitivity tube.

- SVM Anomaly Detection Models
Oracle Data Mining uses one-class SVM for Anomaly Detection (AD).

Related Topics

- Test Node

- Testing Classification Models

- Testing Regression Models

SVM Classification Models

SVM Classification (SVMC) is based on the concept of decision planes that define decision boundaries.

A decision plane is one that separates between a set of objects having different class memberships. SVM finds the vectors (support vectors) that define the separators giving the widest separation of classes.

SVMC supports both binary and multiclass targets.

To build and test an SVMC model, use a Classification node. By default, the SVMC Node tests the models that it builds. Test data is created by splitting the input data into build and test subsets. You can also test a model using a Test node.

After you test an SVMC model, you can tune it.

SVMC uses SVM Weights to specify the relative importance of target values.

- SVM Weights

Related Topics

- Classification Node

- Test Node

- Tuning Classification Models

SVM Weights

SVM models are automatically initialized to achieve the best average prediction across all classes. If the training data does not represent a realistic distribution, then you can bias the model to compensate for class values that are under-represented. If you increase the weight for a class, then the percentage of correct predictions for that class should increase.

SVM Regression Models

SVM Regression (SVMR) models tries to find a continuous function such that the maximum number of data points lie within the epsilon-wide insensitivity tube.

SVM uses an epsilon-insensitive loss function to solve regression problems. Predictions falling within epsilon distance of the true target value are not interpreted as errors.

The epsilon factor is a regularization setting for SVMR. It balances the margin of error with model robustness to achieve the best generalization to new data.

To build and test an SVMR model, use a Regression node. By default, the Regression Node tests the models that it builds. Test data is created by splitting the input data into build and test subsets. You can also test a model using a Test node.

Related Topics

- Regression Node

- Test Node

- Testing Regression Models

SVM Anomaly Detection Models

Oracle Data Mining uses one-class SVM for Anomaly Detection (AD).

There is no target for Anomaly Detection. To build an AD model, use an Anomaly Detection node connected to an appropriate data source.

Related Topics

- Anomaly Detection Node

- Anomaly Detection

Applying SVM Models

You apply a model to new data to predict behavior.

Use an Apply node to apply an SVM model.

You can apply all three kinds of SVM models.

- Applying One-Class SVM Models
One-class SVM models, when applied, produce a prediction and a probability for each case in the scoring data.

Applying One-Class SVM Models

One-class SVM models, when applied, produce a prediction and a probability for each case in the scoring data.

This behavior reflects the fact that the model is trained with normal data.

- If the prediction is 1, then the case is considered typical.

- If the prediction is 0, then the case is considered anomalous.

SVM Classification Algorithm Settings

The settings that you can specify for the Support Vector Machine (SVM) algorithm depend on the Kernel function that you select.

The meaning of the individual settings is the same for both Classification and Regression.

To edit settings SVM Classification algorithm settings:

1. You can edit the settings by using one of the following options:

- Right-click the Classification node and select Advanced Settings.

- Right-click the Classification node and select Edit. Then, click Advanced.

2. In the Algorithm Settings tab, the settings are available. Select the Kernel Function. The options are:

- System determined: (Default). After the model is built, the kernel used is displayed in the settings in the model viewer.

- Linear: If SVM uses the linear kernel, then the model generates coefficients.

- Gaussian (a non-linear function).

3. Click OK after you are done.

- Algorithm Settings for Linear or System Determined Kernel (SVMC)
Lists the algorithm settings for SVM Classification model, if linear kernel is specified.

- Algorithm Settings for Gaussian Kernel (SVMC)
Lists the algorithm settings for SVM Classification model, if Gaussian kernel is specified.

Related Topics

- Algorithm Settings for Linear or System Determined Kernel (SVMR)

- SVM Kernel Functions

Algorithm Settings for Linear or System Determined Kernel (SVMC)

Lists the algorithm settings for SVM Classification model, if linear kernel is specified.

If you specify a linear kernel or if you let the system determine the kernel, then you can change the following settings:

- Tolerance Value

- Complexity Factor

- Active Learning

- Solver: Displays the list of SVM solvers.

- System Determined (default)

- Sub-Gradient Descend. To specify the settings for Sub-Gradient Descend solver, click Option. The Solver (Sub-Gradient Descend) dialog box opens.

- Interior Point Method

Note:
The Solver cannot be selected if the kernel is non-linear.

- Number of Iterations: Sets the upper limit on the number of SVM iterations.

- System Determined

- User Specified

- Solver (Sub-Gradient Descend)
You can specify settings for Sub-Gradient Descend in the Solver Options dialog box.

Related Topics

- Tolerance Value

- Complexity Factor

- Active Learning

Solver (Sub-Gradient Descend)

You can specify settings for Sub-Gradient Descend in the Solver Options dialog box.

Specify the following settings for Sub-Gradient Descend:

1. Regularizer: Controls the type of regularization used by the Support Vector Machine solver. The setting can be used only for linear SVM models. Options are:

- System Determined

- L1

- L2

2. Batch Rows: Sets the batch size for the Support Vector Machine solver. Options are:

- System Determined

- Default: 2000

3. Click OK.

Algorithm Settings for Gaussian Kernel (SVMC)

Lists the algorithm settings for SVM Classification model, if Gaussian kernel is specified.

If you specify the Gaussian kernel, then you can change the following settings:

- Tolerance Value

- Complexity Factor

- Active Learning

Note:

Active Learning is not supported in Oracle Data Miner 17.3 connected to Oracle Database 12.2 and later.

- Standard Deviation (Gaussian Kernel)

- Cache Size (Gaussian Kernel)

- Solver: Displays the list of SVM solvers for Gaussian kernel.

- System Determined

- Interior Point Method

- Number of Iterations: Sets the upper limit on the number of SVM iterations.

- System Determined

- User Specified

- Number of Pivots used in the Incomplete Cholesky Decomposition: Sets the upper limit on the number of pivots used in the incomplete Cholesky decomposition. It is applicable only for non-linear kernels. The value must be a positive integer in the range 1 to 10000. Default is 200.

- Active Learning
Active Learning is a methodology optimizes the selection of a subset of the support vectors that maintain accuracy while enhancing the speed of the model.

- Complexity Factor
The complexity factor determines the trade-off between minimizing model error on the training data and minimizing model complexity.

- Tolerance Value
Tolerance Value is the maximum size of a violation of convergence criteria such that the model is considered to have converged.

- Cache Size (Gaussian Kernel)
If you select the Gaussian kernel, then you can specify a cache size for the size of the cache used for storing computed kernels during the build operation.

- Standard Deviation (Gaussian Kernel)
Standard Deviation is a measure that is used to quantify the amount of variation.

Active Learning

Active Learning is a methodology optimizes the selection of a subset of the support vectors that maintain accuracy while enhancing the speed of the model.

Note:
Active Learning is not supported in Oracle Data Miner 17.3 connected to Oracle Database 12.2 and later.

The key features of Active Learning are:

- Increases performance for a linear kernel. Active learning both increases performance and reduces the size of the Gaussian kernel. This is an important consideration if memory and temporary disk space are issues.

- Forces the SVM algorithm to restrict learning to the most informative examples and not to attempt to use the entire body of data. Usually, the resulting models have predictive accuracy comparable to that of the standard (exact) SVM model.

You should not disable this setting

Active Learning is selected by default. It can be turned off by deselecting Active Learning.

Complexity Factor

The complexity factor determines the trade-off between minimizing model error on the training data and minimizing model complexity.

Its responsibility is to avoid over-fit (an over-complex model fitting noise in the training data) and under-fit (a model that is too simple). The default is to not specify a complexity factor.

You specify the complexity factor for an SVM model by selecting Specify the complexity factors.

A very large value of the complexity factor places an extreme penalty on errors, so that SVM seeks a perfect separation of target classes. A small value for the complexity factor places a low penalty on errors and high constraints on the model parameters, which can lead to under-fit.

If the histogram of the target attribute is skewed to the left or to the right, then try increasing complexity.

The default is to specify no complexity factor, in which case the system calculates a complexity factor. If you do specify a complexity factor, then specify a positive number. If you specify a complexity factor for Anomaly Detection, then the default is 1.

Tolerance Value

Tolerance Value is the maximum size of a violation of convergence criteria such that the model is considered to have converged.

The default value is 0.001. Larger values imply faster building but less accurate models.

Cache Size (Gaussian Kernel)

If you select the Gaussian kernel, then you can specify a cache size for the size of the cache used for storing computed kernels during the build operation.

The default size is 50 megabytes.

The most expensive operation in building a Gaussian SVM model is the computation of kernels. The general approach taken to build is to converge within a chunk of data at a time, then to test for violators outside of the chunk. The build is complete when there are no more violators within tolerance. The size of the chunk is chosen such that the associated kernels can be maintained in memory in a Kernel Cache. The larger the chunk size, the better the chunk represents the population of training data and the fewer number of times new chunks must be created. Generally, larger caches imply faster builds.

Standard Deviation (Gaussian Kernel)

Standard Deviation is a measure that is used to quantify the amount of variation.

If you select the Gaussian kernel, then you can specify the standard deviation of the Gaussian kernel. This value must be positive. The default is to not specify the standard deviation.

For Anomaly Detection, if you specify Standard Deviation, then the default is 1.

SVM Classification Test Viewer

By default, any Classification or Regression model is automatically tested. You have the option to view the test results.

A Classification model is tested by comparing the predictions of the model with known results. Oracle Data Miner keeps the latest test result.

To view the test results for a model, right-click the build node and select View Results.

Related Topics

- Testing Classification Models

SVM Classification Model Viewer

You can examine the details of a SVM Classification model in the SVM Model Viewer.

The tabs displayed in a SVMC model viewer depend on the kernel used to build the model:

- SVMC model viewer for models with Linear Kernel

- SVMC model viewer for models with Gaussian Kernel

- SVMC Model Viewer for Models with Linear Kernel
Lists the tabs that are available if the Support Vector Machines Classification model has a Linear kernel.

- SVMC Model Viewer for Models with Gaussian Kernel
Lists the tabs that are available if the Support Vector Machines Classification model has a Gaussian kernel.

- Coefficients (SVMC Linear)
Support Vector Machine Models built with the Linear Kernel have coefficients. The coefficients are real numbers. The number of coefficients may be quite large.

- Compare (SVMC Linear)
Support Vector Machine Models built with the Linear kernel allow the comparison of target values. You can compare target values.

- Settings (SVMC)
The Settings tab contains information related to the model summary, inputs, target values, cost matrix (if the model is tuned), partition keys (if the model is partitioned) and so on.

- Algorithm Settings for SVMC

Related Topics

- Viewing Models in Model Viewer

SVMC Model Viewer for Models with Linear Kernel

Lists the tabs that are available if the Support Vector Machines Classification model has a Linear kernel.

The tabs are:

- Coefficients (SVMC Linear)
Support Vector Machine Models built with the Linear Kernel have coefficients. The coefficients are real numbers. The number of coefficients may be quite large.

- Compare (SVMC Linear)
Support Vector Machine Models built with the Linear kernel allow the comparison of target values. You can compare target values.

- Settings (SVMC)
The Settings tab contains information related to the model summary, inputs, target values, cost matrix (if the model is tuned), partition keys (if the model is partitioned) and so on.

Coefficients (SVMC Linear)

Support Vector Machine Models built with the Linear Kernel have coefficients. The coefficients are real numbers. The number of coefficients may be quite large.

The Coefficients tab enables you to view SVM coefficients. The viewer supports sorting to specify the order in which coefficients are displayed and filtering to select which coefficients to display.

Coefficients are displayed in the Coefficients Grid. The relative value of coefficients is shown graphically as a bar, with different colors for positive and negative values. For numbers close to zero, the bar may be too small to be displayed.

Compare (SVMC Linear)

Support Vector Machine Models built with the Linear kernel allow the comparison of target values. You can compare target values.

For selected attributes, Data Miner calculates the propensity, that is, the natural inclination or preference to favor one of two target values. For example, propensity for target value 1 is the propensity to favor target value 1.

To compare target values:

1. Select how to display information:

- Fetch Size: The default fetch size is 1000 attributes. You can change this number.

- Sort by absolute value: This is the default. You can deselect this option.

2. Select two distinct target values to compare:

- Target Value 1: Select the first target value.

- Target Value 2: Select the second target value.

3. Click Query. If you have not changed any defaults, then this step is not necessary.

The following information is displayed in the grid:

- Attribute: The name of the attribute.

- Value: Value of the attribute

- Propensity for Target_Value_1: Propensity to favor Target Value 1.

- Propensity for Target_Value_2: Propensity to favor Target Value 2.

Settings (SVMC)

The Settings tab contains information related to the model summary, inputs, target values, cost matrix (if the model is tuned), partition keys (if the model is partitioned) and so on.

In the Partition field, click the partition name. The partition detail is displayed in the Partition Details window.

Click to open the Select Partition dialog box.

The Settings tab displays information about how the model was built:

SVMC Model Viewer for Models with Gaussian Kernel

Lists the tabs that are available if the Support Vector Machines Classification model has a Gaussian kernel.

The tabs are:

- Summary (SVMC)
The Summary tab contains information related to inputs, build details, algorithm settings, and other general settings.

- Inputs (SVMC)
The Inputs tab displays the list of attributes that are used to build the model.

- Weights (SVMC)
In Support Vector Machines classifications, weights are a biasing mechanism for specifying the relative importance of target values (classes).

- Target Values (SVMC)
Target Values for Support Vector Machine for Classification models show the values of the target attributes.

Summary (SVMC)

The Summary tab contains information related to inputs, build details, algorithm settings, and other general settings.

- General settings list the following:

- Type of model (Classification, Regression, and so on)

- Owner of the model (the Schema where the model was built)

- Model Name

- Creation Date

- Duration of the model build (in minutes)

- Size of the model (in MB)

- Comments.

- Algorithm Settings tab lists the following:

- The name of the algorithm used to build the model.

- The algorithm settings that control the model build.

- Build Details displays computed settings. Computed settings are generated by Oracle Data Mining when the model is created.

Related Topics

- General Settings

- SVM Classification Algorithm Settings

Inputs (SVMC)

The Inputs tab displays the list of attributes that are used to build the model.

For each attribute the following information is displayed:

- Name: The name of the attribute.

- Data Type: The data type of the attribute.

- Mining Type: Categorical or Numerical.

- Target: The icon indicates that the attribute is a target attribute.

- Data Preparation: YES indicates that data preparation was performed. It helps to distinguish between User and Auto Data Preparation (ADP) as ADP could be turned off, but the user could still embedded a transformation. If Data Preparation is indicated as YES, then select the column and click it. Each group can contain an Input and a Reverse Expression. If there is no Reverse Expression, then it is not displayed. If there is no Input, then nothing is displayed. Transformations are displayed in SQL notation.

- Partition Key: YES indicates that the attribute is a partition key.

Weights (SVMC)

In Support Vector Machines classifications, weights are a biasing mechanism for specifying the relative importance of target values (classes).

SVM models are automatically initialized to achieve the best average prediction across all classes. However, if the training data does not represent a realistic distribution, then you can bias the model to compensate for class values that are underrepresented. If you increase the weight for a class, then the percentage of correct predictions for that class should increase.

Target Values (SVMC)

Target Values for Support Vector Machine for Classification models show the values of the target attributes.

- Click to search for target values.

- Click to clear search.

Coefficients (SVMC Linear)

Support Vector Machine Models built with the Linear Kernel have coefficients. The coefficients are real numbers. The number of coefficients may be quite large.

The Coefficients tab enables you to view SVM coefficients. The viewer supports sorting to specify the order in which coefficients are displayed and filtering to select which coefficients to display.

Coefficients are displayed in the Coefficients Grid. The relative value of coefficients is shown graphically as a bar, with different colors for positive and negative values. For numbers close to zero, the bar may be too small to be displayed.

- Coefficients Grid (SVMC)

Coefficients Grid (SVMC)

The coefficients grid has these controls:

- Target Value: Select a specific target value and see the coefficients associated with that value. The default is to display the coefficients for the value that occurs least frequently.

- Sort By Absolute Value: If selected, then coefficients are sorted by absolute value. If you sort by absolute value, then a coefficient of -2 comes before a coefficient of 1.9. The default is to sort by absolute value.

- Fetch Size: The number of rows displayed. To figure out if all the coefficients are displayed, choose a fetch size that is greater than the number of rows displayed.

You can search for attributes by name. Use . If no items are listed in the grid, then there are no coefficients for the selected target value. The coefficients grid has these columns:

- Attribute: Name of the attribute.

- Value : Value of the attribute. If the attribute is binned, then this may be a range.

- Coefficient: The probability for the value of the attribute.
The value is shown as a bar with the value centered in the bar. Positive values are light blue; negative values are red.

Compare (SVMC Linear)

Support Vector Machine Models built with the Linear kernel allow the comparison of target values. You can compare target values.

For selected attributes, Data Miner calculates the propensity, that is, the natural inclination or preference to favor one of two target values. For example, propensity for target value 1 is the propensity to favor target value 1.

To compare target values:

1. Select how to display information:

- Fetch Size: The default fetch size is 1000 attributes. You can change this number.

- Sort by absolute value: This is the default. You can deselect this option.

2. Select two distinct target values to compare:

- Target Value 1: Select the first target value.

- Target Value 2: Select the second target value.

3. Click Query. If you have not changed any defaults, then this step is not necessary.

The following information is displayed in the grid:

- Attribute: The name of the attribute.

- Value: Value of the attribute

- Propensity for Target_Value_1: Propensity to favor Target Value 1.

- Propensity for Target_Value_2: Propensity to favor Target Value 2.

- Search

- Propensity

Search

Use to search the grid.

You can search by name (the default), by value, and by propensity for Target Value 1 or propensity for Target Value 2.

- To select a different search option, click the triangle beside the binoculars.

- To clear a search, click .

Propensity

Propensity is intended to show for a given attribute/value pair, which of the two target values has more predictive relationship. Propensity can be measured in terms of being predicted for or against a target value. If propensity is against a value, then the number is negative.

Settings (SVMC)

The Settings tab contains information related to the model summary, inputs, target values, cost matrix (if the model is tuned), partition keys (if the model is partitioned) and so on.

In the Partition field, click the partition name. The partition detail is displayed in the Partition Details window.

Click to open the Select Partition dialog box.

The Settings tab displays information about how the model was built:

- Summary (SVMC)
The Summary tab contains information related to inputs, build details, algorithm settings, and other general settings.

- Inputs (SVMC)
The Inputs tab displays the list of attributes that are used to build the model.

- Partition Keys
The Partition Keys tab lists the columns that are partitioned.

- Weights (SVMC)
In Support Vector Machines classifications, weights are a biasing mechanism for specifying the relative importance of target values (classes).

Summary (SVMC)

The Summary tab contains information related to inputs, build details, algorithm settings, and other general settings.

- General settings list the following:

- Type of model (Classification, Regression, and so on)

- Owner of the model (the Schema where the model was built)

- Model Name

- Creation Date

- Duration of the model build (in minutes)

- Size of the model (in MB)

- Comments.

- Algorithm Settings tab lists the following:

- The name of the algorithm used to build the model.

- The algorithm settings that control the model build.

- Build Details displays computed settings. Computed settings are generated by Oracle Data Mining when the model is created.

- Settings (SVMC Linear)

Related Topics

- General Settings

- SVM Classification Algorithm Settings

Settings (SVMC Linear)

The Settings tab comprises the following:

- Summary (SVMC)

- Inputs (SVMC)

- Target Values (SVMC)

Inputs (SVMC)

The Inputs tab displays the list of attributes that are used to build the model.

For each attribute the following information is displayed:

- Name: The name of the attribute.

- Data Type: The data type of the attribute.

- Mining Type: Categorical or Numerical.

- Target: The icon indicates that the attribute is a target attribute.

- Data Preparation: YES indicates that data preparation was performed. It helps to distinguish between User and Auto Data Preparation (ADP) as ADP could be turned off, but the user could still embedded a transformation. If Data Preparation is indicated as YES, then select the column and click it. Each group can contain an Input and a Reverse Expression. If there is no Reverse Expression, then it is not displayed. If there is no Input, then nothing is displayed. Transformations are displayed in SQL notation.

- Partition Key: YES indicates that the attribute is a partition key.

- Target Values (SVMC)
Target Values for Support Vector Machine for Classification models show the values of the target attributes.

Target Values (SVMC)

Target Values for Support Vector Machine for Classification models show the values of the target attributes.

- Click to search for target values.

- Click to clear search.

Partition Keys

The Partition Keys tab lists the columns that are partitioned.

Along with the partitioned columns, the Partition Keys tab lists the following details:

- Partition Name

- Source

- Data Type

- Value

Weights (SVMC)

In Support Vector Machines classifications, weights are a biasing mechanism for specifying the relative importance of target values (classes).

SVM models are automatically initialized to achieve the best average prediction across all classes. However, if the training data does not represent a realistic distribution, then you can bias the model to compensate for class values that are underrepresented. If you increase the weight for a class, then the percentage of correct predictions for that class should increase.

Algorithm Settings for SVMC

For Classification, the SVM algorithm has these settings:

- Algorithm Name: Support Vector Machine

- Kernel Function: Gaussian or Linear

- Tolerance Value: The default is 0.001

- Specify Complexity Factor: By default, it is not specified.

- Active Learning: ON

- Standard Deviation (Gaussian Kernel only)

- Cache Size (Gaussian Kernel only)

Related Topics

- SVM Classification Algorithm Settings

SVM Regression Algorithm Settings

The settings that you can specify for the Support Vector Machine (SVM) algorithm depend on the Kernel function that you select.

The meaning of the individual settings is the same for both Classification and Regression.

To edit the SVM Regression Algorithm settings:

1. You can edit the settings by using one of the following options:

- Right-click the Classification node and select Advanced Settings.

- Right-click the Classification node and select Edit. Then click Advanced.

2. In the Algorithm Settings tab, the settings are available. Select Kernel Function. The options are:

- System determined (Default). After the model is build, the kernel used is displayed in the settings in the model viewer.

- Linear. If SVM uses the linear kernel, then the model generates coefficients.

- Gaussian (a non-linear function).

3. Click OK after you are done.

- Algorithm Settings for Linear or System Determined Kernel (SVMR)
If you specify a linear kernel or if you let the system determine the kernel, then you can change the Tolerance Value, complexity Factor, and Active Learning for an SVM Regression model.

- Algorithm Settings for Gaussian Kernel (SVMR)
If you specify the Gaussian kernel, then you can edit the Tolerance Value, Complexity Factor, Active Learning, Standard Deviation, and Cache Size for a SVM Regression model.

- Automatic Data Preparation
Most algorithms require some form of data transformation. During the model building process, Oracle Data Mining can automatically perform the transformations required by the algorithm.

Algorithm Settings for Linear or System Determined Kernel (SVMR)

If you specify a linear kernel or if you let the system determine the kernel, then you can change the Tolerance Value, complexity Factor, and Active Learning for an SVM Regression model.

- Tolerance Value
Tolerance Value is the maximum size of a violation of convergence criteria such that the model is considered to have converged.

- Active Learning
Active Learning is a methodology optimizes the selection of a subset of the support vectors that maintain accuracy while enhancing the speed of the model.

- Complexity Factor
The complexity factor determines the trade-off between minimizing model error on the training data and minimizing model complexity.

Tolerance Value

Tolerance Value is the maximum size of a violation of convergence criteria such that the model is considered to have converged.

The default value is 0.001. Larger values imply faster building but less accurate models.

Active Learning

Active Learning is a methodology optimizes the selection of a subset of the support vectors that maintain accuracy while enhancing the speed of the model.

Note:
Active Learning is not supported in Oracle Data Miner 17.3 connected to Oracle Database 12.2 and later.

The key features of Active Learning are:

- Increases performance for a linear kernel. Active learning both increases performance and reduces the size of the Gaussian kernel. This is an important consideration if memory and temporary disk space are issues.

- Forces the SVM algorithm to restrict learning to the most informative examples and not to attempt to use the entire body of data. Usually, the resulting models have predictive accuracy comparable to that of the standard (exact) SVM model.

You should not disable this setting

Active Learning is selected by default. It can be turned off by deselecting Active Learning.

Complexity Factor

The complexity factor determines the trade-off between minimizing model error on the training data and minimizing model complexity.

Its responsibility is to avoid over-fit (an over-complex model fitting noise in the training data) and under-fit (a model that is too simple). The default is to not specify a complexity factor.

You specify the complexity factor for an SVM model by selecting Specify the complexity factors.

A very large value of the complexity factor places an extreme penalty on errors, so that SVM seeks a perfect separation of target classes. A small value for the complexity factor places a low penalty on errors and high constraints on the model parameters, which can lead to under-fit.

If the histogram of the target attribute is skewed to the left or to the right, then try increasing complexity.

The default is to specify no complexity factor, in which case the system calculates a complexity factor. If you do specify a complexity factor, then specify a positive number. If you specify a complexity factor for Anomaly Detection, then the default is 1.

Algorithm Settings for Gaussian Kernel (SVMR)

If you specify the Gaussian kernel, then you can edit the Tolerance Value, Complexity Factor, Active Learning, Standard Deviation, and Cache Size for a SVM Regression model.

- Tolerance Value
Tolerance Value is the maximum size of a violation of convergence criteria such that the model is considered to have converged.

- Complexity Factor
The complexity factor determines the trade-off between minimizing model error on the training data and minimizing model complexity.

- Active Learning
Active Learning is a methodology optimizes the selection of a subset of the support vectors that maintain accuracy while enhancing the speed of the model.

- Standard Deviation (Gaussian Kernel)
Standard Deviation is a measure that is used to quantify the amount of variation.

- Cache Size (Gaussian Kernel)
If you select the Gaussian kernel, then you can specify a cache size for the size of the cache used for storing computed kernels during the build operation.

Tolerance Value

Tolerance Value is the maximum size of a violation of convergence criteria such that the model is considered to have converged.

The default value is 0.001. Larger values imply faster building but less accurate models.

Complexity Factor

The complexity factor determines the trade-off between minimizing model error on the training data and minimizing model complexity.

Its responsibility is to avoid over-fit (an over-complex model fitting noise in the training data) and under-fit (a model that is too simple). The default is to not specify a complexity factor.

You specify the complexity factor for an SVM model by selecting Specify the complexity factors.

A very large value of the complexity factor places an extreme penalty on errors, so that SVM seeks a perfect separation of target classes. A small value for the complexity factor places a low penalty on errors and high constraints on the model parameters, which can lead to under-fit.

If the histogram of the target attribute is skewed to the left or to the right, then try increasing complexity.

The default is to specify no complexity factor, in which case the system calculates a complexity factor. If you do specify a complexity factor, then specify a positive number. If you specify a complexity factor for Anomaly Detection, then the default is 1.

Active Learning

Active Learning is a methodology optimizes the selection of a subset of the support vectors that maintain accuracy while enhancing the speed of the model.

Note:
Active Learning is not supported in Oracle Data Miner 17.3 connected to Oracle Database 12.2 and later.

The key features of Active Learning are:

- Increases performance for a linear kernel. Active learning both increases performance and reduces the size of the Gaussian kernel. This is an important consideration if memory and temporary disk space are issues.

- Forces the SVM algorithm to restrict learning to the most informative examples and not to attempt to use the entire body of data. Usually, the resulting models have predictive accuracy comparable to that of the standard (exact) SVM model.

You should not disable this setting

Active Learning is selected by default. It can be turned off by deselecting Active Learning.

Standard Deviation (Gaussian Kernel)

Standard Deviation is a measure that is used to quantify the amount of variation.

If you select the Gaussian kernel, then you can specify the standard deviation of the Gaussian kernel. This value must be positive. The default is to not specify the standard deviation.

For Anomaly Detection, if you specify Standard Deviation, then the default is 1.

Cache Size (Gaussian Kernel)

If you select the Gaussian kernel, then you can specify a cache size for the size of the cache used for storing computed kernels during the build operation.

The default size is 50 megabytes.

The most expensive operation in building a Gaussian SVM model is the computation of kernels. The general approach taken to build is to converge within a chunk of data at a time, then to test for violators outside of the chunk. The build is complete when there are no more violators within tolerance. The size of the chunk is chosen such that the associated kernels can be maintained in memory in a Kernel Cache. The larger the chunk size, the better the chunk represents the population of training data and the fewer number of times new chunks must be created. Generally, larger caches imply faster builds.

Automatic Data Preparation

Most algorithms require some form of data transformation. During the model building process, Oracle Data Mining can automatically perform the transformations required by the algorithm.

You can supplement the automatic transformations with additional transformations of your own, or you can manage all the transformations yourself.

In calculating automatic transformations, Oracle Data Mining uses heuristics that address the common requirements of a given algorithm. This process results in reasonable model quality mostly.

SVM Regression Test Viewer

By default, any Classification or Regression model is automatically tested. You have the option to view the test results.

A Classification model is tested by comparing the predictions of the model with known results. Oracle Data Miner keeps the latest test result.

To view the test results for a model, right-click the Build node and select View Results.

Related Topics

- Testing Regression Models

SVM Regression Model Viewer

You can examine a SVM (Regression) Model in the SVM Regression Model Viewer.

The information displayed in the model viewer depends on which kernel was used to build the model.

- If the Gaussian kernel was used, then there is only the Settings tab.

- If the Linear Kernel was used, then there are three tabs: Coefficients,Compare, and Settings,

The tabs displayed in a SVMC model viewer depend on the kernel used to build the model:

- SVMR model viewer for models with Linear Kernel

- SVMR model viewer for models with Gaussian Kernel

- SVMR Model Viewer for Models with Linear Kernel
Lists the tabs that are available if the Support Vector Machines for Regression model has a Linear kernel.

- SVMR Model Viewer for Models with Gaussian Kernel
Lists the tabs that are available if the Support Vector Machines for Regression model has a Gaussian kernel.

Related Topics

- Viewing Models in Model Viewer

SVMR Model Viewer for Models with Linear Kernel

Lists the tabs that are available if the Support Vector Machines for Regression model has a Linear kernel.

The tabs are:

- Coefficients (SVMR)
The Coefficients tab enables you to view SVMR coefficients.

- Settings (SVMR)
The Settings tab displays information about how the model was built.

Coefficients (SVMR)

The Coefficients tab enables you to view SVMR coefficients.

Support Vector Machine Models built with the Linear Kernel have coefficients. The coefficients are real numbers. The number of coefficients may be quite large.

The viewer supports sorting to specify the order in which coefficients are displayed and filtering to select which coefficients to display.

The coefficients are displayed in the SVMR Coefficients Grid. The relative value of the coefficients is shown graphically as a bar, with different colors for positive and negative values. For numbers close to zero, the bar may be too small to be displayed.

- SVMR Coefficients Grid

SVMR Coefficients Grid

Information about coefficients is organized as follows:

- Sort by Absolute Value: The default is to sort by absolute value. For example 1 and -1 have the same absolute value. If you change this value, then you must click Query.

- Fetch Size: It is the maximum number of rows to fetch; the default is 1,000. Smaller values result in faster fetches. If you change this value, then you must click Query.

- Coefficients: The number of coefficients displayed; for example, 95 out of 95, indicating that there are 95 coefficients and all 95 of them are displayed.

You can perform the following tasks:

- Search: Use to search for items. You can search by:

- Attribute name (Default)

- Value

- Coefficient

- All (AND): If you search by this criteria, then you search for items that satisfy all criteria specified. For example, a search for ED Bac finds all attributes where both values appear.

- All (Or): If you search by this criteria, then you search for attributes that include at least one value

- Clear search: To clear a search, click .

- To select a different search option, click the triangle beside the binoculars.

Coefficients are listed in a grid. The coefficients grid has these columns:

- Attribute: Name of the attribute

- Value: Value of the attribute

- Coefficient: The value of each coefficient for the selected target value is displayed. A bar is shown in front of (and possible overlapping) each coefficient. The bar indicates the relative size of the coefficient. For positive values, the bar is light blue; for negative values, the bar is red. If a value is close to 0, then the bar may be too small to be displayed.

Settings (SVMR)

The Settings tab displays information about how the model was built.

The information is displayed in the following tabs:

- Summary tab: Contains the Model and Algorithm settings.

- Inputs tab: Contains the attributes used to build the model.

SVMR Model Viewer for Models with Gaussian Kernel

Lists the tabs that are available if the Support Vector Machines for Regression model has a Gaussian kernel.

The tabs are:

- Summary (SVMR)
The Summary tab contains information related to inputs, build details, algorithm settings, and other general settings.

- Inputs (SVMR)
The Inputs tab displays the list of attributes that are used to build the model.

Summary (SVMR)

The Summary tab contains information related to inputs, build details, algorithm settings, and other general settings.

- General settings list the following:

- Type of model (Classification, Regression, and so on)

- Owner of the model (the Schema where the model was built)

- Model Name

- Creation Date

- Duration of the model build (in minutes)

- Size of the model (in MB)

- Comments.

- Algorithm Settings section lists the following:

- The name of the algorithm used to build the model.

- The algorithm settings that control the model build.

- Build Details displays computed settings. Computed settings are generated by Oracle Data Mining when the model is created.

Related Topics

- General Settings

- SVM Regression Algorithm Settings

Inputs (SVMR)

The Inputs tab displays the list of attributes that are used to build the model.

For each attribute, the following information is displayed:

- Name: The name of the attribute.

- Data Type: The data type of the attribute.

- Mining Type: Categorical or Numerical.

- Target: The icon indicates that the attribute is a target attribute.

- Data Preparation: YES indicates that data preparation was performed. It helps to distinguish between User and Auto Data Preparation (ADP) as ADP could be turned off, but the user could still embedded a transformation. If Data Preparation is indicated as YES, then select the column and click it. Each group can contain an Input and a Reverse Expression. If there is no Reverse Expression, then it is not displayed. If there is no Input, then nothing is displayed. Transformations are displayed in SQL notation.

- Partition Key: YES indicates that the attribute is a partition key.

Settings Information

Certain settings related to automatic data preparation. Epsilon Value, Support and Confidence are common to most algorithms.

This section contains topics about settings that are common to most algorithms:

- General Settings
The generic settings are contained in the Settings tab and General tab.

- Automatic Data Preparation
In calculating automatic transformations, Oracle Data Mining uses heuristics that address the common requirements of a given algorithm. This process results in reasonable model quality in most cases.

- Other Settings
The other settings are related to the number of attributes in a rule, data preparation, support, and confidence.

- Epsilon Value
Support Vector Machine makes a distinction between small errors and large errors. The difference is defined by the epsilon value.

General Settings

The generic settings are contained in the Settings tab and General tab.

The Settings tab of a model viewer displays settings in three categories:

- General displays generic information about the model, as described in this topic.

- Algorithm Settings displays information that are specific to the selected algorithm.

- Build Details displays computed settings. Computed settings are generated by Oracle Data Mining when the model is created.

The General tab contains the following information for all algorithms:

- Type The mining function for the model: anomaly detection, association rules, attribute importance, classification, clustering, feature extraction, or regression.

- Owner: The data mining account (schema) used to build the model.

- Model Name: The name of the model.

- Target Attribute: The target attribute; only Classification and Regression models have targets.

- Creation Date: The date when the model was created in the form MM/DD/YYYY

- Duration: Time in minutes required to build model.

- Size: The size of the model in megabytes.

- Comment: For models not created using Oracle Data Miner, this option displays comments embedded in the models. To see comments for models built using Oracle Data Miner, go to Properties for the node where the model is built.
Models created using Oracle Data Miner may contain BALANCED, NATURAL, CUSTOM, or TUNED. Oracle Data Miner inserts these values to indicate if the model has been tuned and in what way it was tuned.

Automatic Data Preparation

In calculating automatic transformations, Oracle Data Mining uses heuristics that address the common requirements of a given algorithm. This process results in reasonable model quality in most cases.

Most algorithms require some form of data transformation. During the model building process, Oracle Data Mining can automatically perform the transformations required by the algorithm. You can choose to supplement the automatic transformations with additional transformations of your own, or you can choose to manage all the transformations yourself.

If Automatic Data Preparation is performed, then the same data preparation is automatically performed for data that is scored using the model. If Automatic Data Preparation is OFF, that is if you manage all the transformations yourself, then you must prepare Apply data in the same way that the Build data was prepared.

Other Settings

The other settings are related to the number of attributes in a rule, data preparation, support, and confidence.

The settings are:

- Limit Number of Attributes in Each Rule: By default, this option is selected. The maximum number of attributes in each rule. This number must be an integer between 2 and 20. Higher numbers of rules result in slower builds. You can change the number of attributes in a rule, or you can specify no limit for the number of attributes in a rule. It is a good practice to start with the default and increase this number slowly.

- To specify no limit, deselect this option.

- Specifying many attributes in each rule increases the number of rules considerably.

- The default is 3.

- Automatic Preparation: ON or OFF. ON signifies that Automatic Data Preparation is used for normalization and outlier detection. The SVM algorithm automatically handles missing value treatment and the transformation of categorical data. Normalization and outlier detection must be handled by ADP or prepared manually. The default is ON.

- Minimum Support: A number between 0 and 100 indicating a percentage. Smaller values for support results in slower builds and requires more system resources. The default is 5%.

- Minimum Confidence: Confidence in the rules. A number between 0 and 100 indicating a percentage. High confidence results in a faster build. The default is 10%.

Related Topics

- Automatic Data Preparation (ADP)

Epsilon Value

Support Vector Machine makes a distinction between small errors and large errors. The difference is defined by the epsilon value.

The algorithm calculates and optimizes an epsilon value internally, or you can supply a value.

You can specify the epsilon value for an SVM model by clicking the option Yes in answer to the question Do you want to specify an epsilon value?.

The epsilon value must be either greater than 0 or undefined

- If the number of support vectors defined by the model is very large, then try a larger epsilon value.

- If there are very high cardinality categorical attributes, then try decreasing epsilon.

By default, no epsilon value is specified. In such a case, the algorithm calculates an epsilon value.

Index

A  B  C  D  E  F  G  I  J  L  M  N  O  P  R  S  T  U  V  W

A

- algorithms

- classification 1

- O-Cluster 1

- algorithm settings

- SVM Regression 1

- System Determined Kernel 1

- Anomaly Detection Node 1

- advanced model settings 1

- context menu 1

- creating 1

- editing 1

- properties 1

- architecture

- Data Miner 1

- area under the curve 1

- Association Node 1

- behavior 1

- context menu 1

- creating 1

- editing 1

- properties 1

- attribute dependency 1 , 2

- Automatic Data Preparation 1

- average accuracy 1

B

- benefit

- tab 1

- binary target 1

- Binning 1

C

- classification

- algorithms 1

- apply 1

- build 1

- overview 1

- score 1

- test 1

- Classification Model

- test viewer 1

- components pane 1

- connection

- creating 1

- properties 1

- connections

- Connections tab 1

- Data Miner tab 1

- editing 1

- managing 1

- removing 1

- tab 1

- viewing 1

- context menu

- Anomaly Detection Node 1

- Association Node 1

- Create Table or View Node 1

- Explore Data Node 1

- Graph Node 1

- JSON Query Node 1

- nodes 1

- Sample Node 1

- SQL Query Node 1

- Update Table Node 1

- workflows 1

- Context Menu

- Data Source Node 1

- cost

- performance measure 1

- tune settings 1

- cost matrix 1

- Costs and Benefits 1

- Create Table or View Data

- viewing 1

- Create Table or View Node

- about 1

- creating 1

- editing 1

- creating

- nodes 1

- project 1

- workflow 1

D

- database roles

- Oracle R Enterprise 1

- Data Miner

- architecture 1

- documentation 1

- Oracle By Example tutorials 1

- overview 1

- data miner project 1

- Data Mining

- forum 1

- process 1

- Data Mining Process 1

- data mining project

- create 1

- data preparation

- automatic 1

- manual 1

- numerical 1

- Data Source Node

- context menu 1

- creating 1

- defining 1

- editing 1

- properties 1

- running 1

- supported data types 1

- viewing 1

- data table names 1

- data types

- Data Source Node 1

- pseudo JSON 1

- Transform Node 1

- Update Table Node 1

- data viewer

- Explore Data Node 1

- Transform Node 1

- Update Table Node 1

- Decision Tree 1

- define

- data source 1

- delete

- project 1

- depenency 1

- deploy

- nodes 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8

- workflow 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8

- deploying workflows

- data query scripts 1

- object generation scripts 1

E

- Epsilon Value 1

- Expectation Maximization 1

- Explore Data Node

- context menu 1

- creating 1

- editing 1

- properties 1

- Explore Node Calculations 1

- exporting

- Explore Node calculations 1

- Expression Builder

- functions 1

F

- Feature Compare node 1

- Feature Extraction 1

- filter column 1

- Filter Columns Details

- about 1

- creating 1

- Filter Rows Node

- about 1

- creating 1

- editing 1

G

- Gaussian Kernel

- algorithm settings 1

- Generalized Linear Models 1 , 2

- Graph

- editing 1

- viewing data 1

- Graph Node

- about 1

- creating 1

- editing 1

- properties 1

- supported data types 1

- types 1

I

- import

- workflow 1

- import requirements

- workflow 1

- In-Memory 1

- interpreting

- cluster rules 1

J

- Join

- about 1

- Join Node

- creating 1

- editing 1 , 2

- JSON

- settings 1

- JSON Query Node

- about 1

- creating 1

- editor 1

L

- Lift 1 , 2

- graph 1

- linear regression 1 , 2

- link

- cancelling 1

- deleting 1

- nodes 1

- Link nodes 1

M

- Model

- properties 1

- models

- Anomaly Detection Node 1

- Association Node 1

- classification 1

- Classification Node 1

- Clustering Node 1

- Feature Extraction Node 1

- Model Details Node 1

- Model Node 1

- Regression Node 1

- SVM 1

- model viewers

- SVM 1

- multiclass target 1

N

- Naive Bayes 1

- nodes

- about 1

- adding 1

- comment 1

- copying 1

- creating 1

- linking 1

- name 1

- position 1

- refreshing 1

- running 1

- states 1

- types 1

- Normalization 1

O

- O-Cluster 1

- algorithm 1

- Oracle Enterprise Manager Jobs 1

- Oracle Text Lexer 1

- Orthogonal Partitioning Clustering 1

- Outlier 1

- overall accuracy 1

- Overview

- Data Miner 1

P

- Parallel Processing

- about 1

- node settings 1

- support 1

- workflow settings 1

- performance matrix 1

- Performance Matrix 1

- Predictive Confidence 1

- formula 1

- prerequisites

- Data Mining 1

- Profit 1 , 2

- using 1

- Profit and ROI 1

- example 1

- use case 1

- profit setting 1

- project

- create 1

- creating new 1

- deleting 1 , 2

- expanding 1

- managing 1

- name restrictions 1

- renaming 1

- Propensity 1

- properties 1

- Anomaly Detection Node 1

- Association Node 1

- Create Table or View Node 1

- Data Source Node 1

- Explore Data Node 1

- Graph Node 1

- JSON Query Node 1

- nodes 1

- Sample Node 1

- SQL Query Node 1

- Update Table Node 1

- workflow 1

- workflows 1

R

- R Build node 1

- receiver operating characteristic

- statistics 1

- Receiver Operating Characteristics 1 , 2

- regression

- apply 1

- build 1

- score 1

- testing 1

- Regression Statistics 1

- rename

- project 1

- Residual 1

- Residual Plot 1

- ROC 1

- tuning steps 1

- Tuning tab 1

- using 1

- ROC curves 1

- R Script

- support 1

S

- Sample Data 1

- Sample Node

- about 1

- creating 1

- editing 1

- properties 1

- scoring 1

- scripts

- data query 1

- object generation 1

- requirements 1

- using SQL*Plus 1

- using SQL worksheet 1

- variable definition 1

- workflow 1

- Select Data Guide 1

- SQL Query

- restrictions 1

- SQL Query Node

- context menu 1

- creating 1

- editor 1

- inputs 1

- properties 1

- SQL tab 1

- SQL Worksheet 1

- supported data types

- Graph Nodes 1

- Support Vector Machine 1 , 2

- SVM algorithm

- algorithms 1

- SVM coefficients 1 , 2 , 3

- SVM models

- coefficients 1

- compare 1 , 2 , 3

- settings 1 , 2 , 3 , 4 , 5 , 6

- SVM Regression

- algorithm settings 1

T

- test

- Classification models 1

- metrics for Classification Models 1

- text concepts

- stoplist 1

- stoptheme 1

- stopword 1

- theme 1

- text mining 1

- Text Mining 1

- Text preparation 1

- Transformation

- custom definitions 1

- defining 1

- Transform Node

- about 1

- Aggregation 1

- creating 1

- editing 1

- Expression Builder 1

- Filter Columns 1

- Transform Nodes

- types 1

- tuning

- Classification Models 1

- removing 1

U

- Update Table node 1

- Update Table Node

- context menu 1

- creating 1

- editing 1

- inputs and outputs 1

- properties 1

V

- viewing

- Data Miner tab 1

W

- workflow

- about 1

- compatibility 1

- creating 1

- deleting 1

- deploying 1

- exporting 1

- importing 1

- import requirements 1

- loading 1

- locking and unlocking 1

- managing 1

- name restrictions 1

- prerequisites 1

- properties 1

- renaming 1

- running 1

- sequence 1

- terminology 1

- thumbnail 1

- thumbnail view 1

- workflow controls

- managing workflows 1

- Workflow Editor 1

- workflow scripts

- requirements 1

- running 1

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