|Oracle® Data Mining User's Guide
12c Release 1 (12.1)
|PDF · Mobi · ePub|
This preface lists changes in this release in Oracle Data Mining User's Guide.
This guide is new in release 12c. Oracle Data Mining User's Guide replaces two manuals that were provided in previous releases: Oracle Data Mining Administrator's Guide and Oracle Data Mining Application Developer's Guide.
Information about database administration for Oracle Data Mining is now consolidated in of Oracle Data Mining User's Guide. The remaining chapters of this guide are devoted to application development.
Information about the Data Mining sample programs is now in Appendix A, "The Data Mining Sample Programs" in this guide.
The following changes are documented in Oracle Data Mining User's Guide for 12c Release 1 (12.1).
The following features are new in this release:
Expanded prediction details
PREDICTION_DETAILS function now supports all predictive algorithms and returns more details about the predictors. New functions,
FEATURE_DETAILS, are introduced.
See "Prediction Details".
The Data Mining SQL functions now support an analytic clause for scoring data dynamically without a pre-defined model.
See "Dynamic Scoring".
Significant enhancements in text mining
This enhancement greatly simplifies the data mining process (model build, deployment and scoring) when unstructured text data is present in the input.
Manual pre-processing of text data is no longer needed.
No text index must be created.
Additional data types are supported:
Character data can be specified as either categorical values or text.
New clustering algorithm: Expectation Maximization
New feature extraction algorithm: Singular Value Decomposition with Principal Component Analysis
Generalized Linear Models are enhanced to support feature selection and creation.
The following features are no longer supported by Oracle. See Oracle Database Upgrade Guide for a complete list of desupported features in this release.
Oracle Data Mining Java API
Adaptive Bayes Network (ABN) algorithm
The following are additional new features in this release:
A new SQL function,
CLUSTER_DISTANCE, is introduced.
CLUSTER_DISTANCE returns the raw distance between each row and the cluster centroid.
New support for native double data types,
BINARY_FLOAT, improves the performance of the SQL scoring functions.
Decision Tree algorithm now supports nested data.