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45 DBMS_DATA_MINING_TRANSFORM

DBMS_DATA_MINING_TRANSFORM implements a set of transformations that are commonly used in data mining and provides a framework that you can use for specifying your own transformations.

See Also:

This chapter contains the following topics:


Using DBMS_DATA_MINING_TRANSFORM

This section contains topics that relate to using the DBMS_DATA_MINING_TRANSFORM package.


Overview

A transformation is a SQL expression that modifies the data in one or more columns.

Data must typically undergo certain transformations before it can be used to build a mining model. Many data mining algorithms have specific transformation requirements.

Data that will be scored must be transformed in the same way as the data that was used to create (train) the model.

External or Embedded Transformations

DBMS_DATA_MINING_TRANSFORM offers two approaches to implementing transformations. For a given model, you can either:

If you create a transformation list and pass it to CREATE_MODEL, the transformation expressions are embedded in the model and automatically implemented whenever the model is applied.

If you create a view, the transformation expressions are external to the model. You will need to re-create the transformations whenever you apply the model.

Note:

Embedded transformations significantly enhance the model's usability while simplifying the process of model management.

Automatic Transformations

Oracle Data Mining supports an Automatic Data Preparation (ADP) mode. When ADP is enabled, most algorithm-specific transformations are automatically embedded. Any additional transformations must be explicitly provided in an embedded transformation list or in a view.

If ADP is enabled and you create a model with a transformation list, both sets of transformations are embedded. The model will execute the user-specified transformations from the transformation list before executing the automatic transformations specified by ADP.

Within a transformation list, you can selectively disable ADP for individual attributes.

See Also:

"Automatic Data Preparation"

Oracle Data Mining Concepts for a list of the algorithm-specific transformations implemented by ADP

"About Transformation Lists"

Transformations in DBMS_DATA_MINING_TRANSFORM

The transformations supported by DBMS_DATA_MINING_TRANSFORM are summarized in this section.

Binning

Binning refers to the mapping of continuous or discrete values to discrete values of reduced cardinality.


Linear Normalization

Normalization is the process of scaling continuous values down to a specific range, often between zero and one. Normalization transforms each numerical value by subtracting a number (the shift) and dividing the result by another number (the scale).

x_new = (x_old-shift)/scale

Outlier Treatment

An outlier is a numerical value that is located far from the rest of the data. Outliers can artificially skew the results of data mining.


Missing Value Treatment

Missing data may indicate sparsity or it may indicate that some values are missing at random. DBMS_DATA_MINING_TRANSFORM supports the following transformations for minimizing the effects of missing values:

Note:

Oracle Data Mining also has default mechanisms for handling missing data. See Oracle Data Mining Application Developer's Guide for details.


Operational Notes

The DBMS_DATA_MINING_TRANSFORM package offers a flexible framework for specifying data transformations. If you choose to embed transformations in the model (the preferred method), you will create a transformation list object and pass it to the CREATE_MODEL Procedure. If you choose to transform the data without embedding, you will create a view.

When specified in a transformation list, the transformation expressions are executed by the model. When specified in a view, the transformation expressions are executed by the view.

Transformation Definitions

Transformation definitions are used to generate the SQL expressions that transform the data. For example, the transformation definitions for normalizing a numeric column are the shift and scale values for that data.

With the DBMS_DATA_MINING_TRANSFORM package, you can call procedures to compute the transformation definitions, or you can compute them yourself, or you can do both.

Transformation Definition Tables

DBMS_DATA_MINING_TRANSFORM provides INSERT procedures that compute transformation definitions and insert them in transformation definition tables. You can modify the values in the transformation definition tables or populate them yourself.

Populated transformation definition tables are required for transforming the data in external views. They are also required by the STACK routines that can be used for building a transformation list. However, STACK routines are not the only method for adding transformation expressions to a transformation list. You can also build a transformation list without using definition tables.

To specify transformations based on definition tables, follow these steps: 

  1. Use CREATE routines to create transformation definition tables.

    The tables have columns to hold the transformation definitions for a given type of transformation. For example, the CREATE_BIN_NUM Procedure creates a definition table that has a column for storing data values and another column for storing the associated bin identifiers.

  2. Use INSERT routines to compute and insert transformation definitions in the tables.

    Each INSERT routine uses a specific technique for computing the transformation definitions. For example, the INSERT_BIN_NUM_EQWIDTH Procedure computes bin boundaries by identifying the minimum and maximum values then setting the bin boundaries at equal intervals.

  3. Use STACK or XFORM routines to generate transformation expressions based on the information in the definition tables:

    • Use STACK routines to add the transformation expressions to a transformation list. Pass the transformation list to the CREATE_MODEL Procedure. The transformation expressions will be assembled into one long SQL query and embedded in the model.

    • Use XFORM routines to execute the transformation expressions within a view. The transformations will be external to the model and will need to be re-created whenever the model is applied to new data.

To specify transformations without using definition tables, you can follow these steps, which transform a single attribute for embedding: 

  1. Write a SQL expression for transforming the attribute.

  2. Write a SQL expression for reversing the transformation.

  3. Specify whether or not to disable ADP for the attribute. (By default ADP is not disabled.)

  4. Call the SET_TRANSFORM Procedure to add the transformation instructions to a transformation list.

  5. Repeat steps 1 through 4 for each attribute that you wish to transform.

  6. Pass the transformation list to the CREATE_MODEL Procedure. The transformation expressions will be assembled into one long SQL query and embedded in the model.

    Note:

    SQL expressions that you specify with SET_TRANSFORM must fit within a VARCHAR2. To specify a longer expression, you can use the SET_EXPRESSION Procedure. With SET_EXPRESSION, you can build an expression by appending rows to a VARCHAR2 array.

    If you use SET_EXPRESSION to construct your own expression, you should also construct a reverse expression. If you wish, you can disable ADP for the attribute. You must write your own code for adding the transformation information to a transformation list.

About Transformation Lists

The elements of a transformation list are transformation records. Each transformation record provides all the information needed by the model for managing the transformation of a single attribute. Each transformation record includes the following fields:

Reverse Transformations

Reverse transformations return the attribute to its original state for scoring and model details (DBMS_DATA_MINING.GET_MODEL_DETAILS_* functions). Reverse transformations support model transparency by keeping embedded transformations internal to the model.

For example, if you specify the transformation expression 'log(10, attr1)'for attribute attr1, you could specify the reverse transformation expression 'power(10, attr1)'.


Disabling Automatic Data Preparation

ADP is controlled by a model-specific setting (PREP_AUTO). The PREP_AUTO setting affects all model attributes unless you disable it for an individual attribute.

See Also:

Table 45-1 for details about the TRANSFORM_LIST and TRANSFORM_REC object types

"Automatic Transformations" for information about the PREP_AUTO setting

"Summary of DBMS_DATA_MINING Subprograms" for links to the model details functions

Oracle Database SQL Language Reference for links to the Data Mining scoring functions


Adding Transformation Records to a Transformation List

A transformation list is a stack of transformation records. When a new transformation record is added, it is appended to the top of the stack. (See "About Stacking" for details.)

When you use SET_TRANSFORM to add a transformation record to a transformation list, you can specify values for all the fields in the transformation record.

When you use STACK procedures to add transformation records to a transformation list, only the transformation expression field, and possibly the reverse transformation expression field, are populated. Normalization transformations can be reversed. Binning, clipping, and missing value transformations cannot be reversed. (See Example 45-1, "Stacking a Clipping Transformation".)

You can use both STACK procedures and SET_TRANSFORM to build one transformation list. Each STACK procedure call adds transformation records for all the attributes in a specified transformation definition list. Each SET_TRANSFORM call adds a transformation record for a single attribute.


About Stacking

Transformation lists are built by stacking transformation records. Transformation lists are evaluated from bottom to top. Each transformation expression depends on the result of the transformation expression below it in the stack.

Stack Procedures

STACK procedures create transformation records from the information in transformation definition tables. For example STACK_BIN_NUM builds a transformation record for each attribute specified in a definition table for numeric binning. STACK procedures stack the transformation records as follows:

Example 45-1 Stacking a Clipping Transformation

This example shows how STACK_CLIP Procedure would add transformation records to a transformation list. Note that the clipping transformations are not reversed in COL1 and COL2 after stacking.

Refer to: 

Assume a clipping definition table populated as follows.

col att lcut lval rcut rval
COL1 null -1.5 -1.5 4.5 4.5
COL2 null 0 0 1 1

Assume the following transformation list before stacking.

-------------------------
transformation record #1:
-------------------------
     attribute_name       = COL1
     attribute_subname    = null
     expression           = log(10, COL1)
     reverse_expression   = power(10, COL1)
-------------------------
transformation record #2:
-------------------------
     attribute_name       = COL3
     attribute_subname    = null
     expression           = ln(COL3)
     reverse_expression   = exp(COL3)

After stacking, the transformation list is as follows.

-------------------------
transformation record #1:
-------------------------
     attribute_name       = COL1
     attribute_subname    = null
     expression           = CASE WHEN log(10, COL1) < -1.5 THEN -1.5
                                 WHEN log(10, COL1) > 4.5  THEN 4.5
                                 ELSE log(10, COL1)
                            END;
     reverse_expression   = power(10, COL1)
-------------------------
transformation record #2:
-------------------------
     attribute_name       = COL3
     attribute_subname    = null
     expression           = ln(COL3)
     reverse_expression   = exp(COL3)
-------------------------
transformation record #3:
-------------------------
     attribute_name       = COL2
     attribute_subname    = null
     expression           = CASE WHEN COL2 < 0 THEN 0
                                 WHEN COL2 > 1 THEN 1
                                 ELSE COL2
                            END;
     reverse_expression   = null

Nested Data Transformations

The CREATE routines create transformation definition tables that include two columns, col and att, for identifying attributes. The column col holds the name of a column in the data table. If the data column is not nested, then att is null, and the name of the attribute is col. If the data column is nested, then att holds the name of the nested attribute, and the name of the attribute is col.att.

The INSERT and XFORM routines ignore the att column in the definition tables. Neither the INSERT nor the XFORM routines support nested data.

Only the STACK procedures and SET_TRANSFORM support nested data. Nested data transformations are always embedded in the model.

Nested columns in Oracle Data Mining can have either of the following types:

DM_NESTED_NUMERICALS
DM_NESTED_CATEGORICALS

See Also:

"Constants"

Oracle Data Mining Application Developer's Guide for details about nested attributes in Oracle Data Mining

Specifying Nested Attributes in a Transformation Record

A transformation record includes two fields, attribute_name and attribute_subname, for identifying the attribute. The field attribute_name holds the name of a column in the data table. If the data column is not nested, then attribute_subname is null, and the name of the attribute is attribute_name. If the data column is nested, then attribute_subname holds the name of the nested attribute, and the name of the attribute is attribute_name.attribute_subname.

Default Nested Transformations

You can specify different transformations for different attributes in a nested column, and you can specify a default transformation for all the remaining attributes in the column. To specify a default nested transformation, specify null in the attribute_name field and the name of the nested column in the attribute_subname field as shown in Example 45-2. Note that the keyword VALUE is used to represent the value of a nested attribute in a transformation expression.

Example 45-2 Transforming a Nested Column

The following statement transforms two of the nested attributes in COL_N1: ATTR1 is transformed with normalization; ATTR2 is set to null, which causes attribute removal transformation (ATTR2 is not used in training the model). All the remaining attributes in COL_N1 are divided by 10.

DECLARE
  stk dbms_data_mining_transform.TRANSFORM_LIST;
BEGIN
  dbms_data_mining_transform.SET_TRANSFORM(
      stk,'COL_N1', 'ATTR1', '(VALUE - (-1.5))/20', 'VALUE *20 + (-1.5)');
  dbms_data_mining_transform.SET_TRANSFORM(
      stk,'COL_N1', 'ATTR2', NULL, NULL);
  dbms_data_mining_transform.SET_TRANSFORM(
      stk, NULL, 'COL_N1', 'VALUE/10', 'VALUE*10');
END;
/

The following SQL is generated from this statement.

CAST(MULTISET(SELECT DM_NESTED_NUMERICAL(
                               "ATTRIBUTE_NAME",
                               DECODE("ATTRIBUTE_NAME",
                                 'ATTR1', ("VALUE" - (-1.5))/20,
                                 "VALUE"/10))
                        FROM TABLE("COL_N1")
                       WHERE "ATTRIBUTE_NAME" IS NOT IN ('ATTR2'))
             AS DM_NESTED_NUMERICALS)

If transformations are not specified for COL_N1.ATTR1 and COL_N1.ATTR2, then the default transformation is used for all the attributes in COL_N1, and the resulting SQL does not include a DECODE.

CAST(MULTISET(SELECT DM_NESTED_NUMERICAL(
                               "ATTRIBUTE_NAME",
                               "VALUE"/10)
                        FROM TABLE("COL_N13")
                       WHERE "ATTRIBUTE_NAME" IS NOT IN ('ATTR2'))
             AS DM_NESTED_NUMERICALS)

Since DECODE is limited to 256 arguments, multiple DECODE functions are nested to support an arbitrary number of individual nested attribute specifications.

Example 45-3 Stacking Nested Normalization Transformations

This example shows how the STACK_NORM_LIN Procedure would add transformation records for nested column COL_N to a transformation list.

Refer to: 

Assume a linear normalization definition table populated as follows.

col att shift scale
COL_N ATT2 0 20
null COL_N 0 10

Assume the following transformation list before stacking.

-------------------------
transformation record #1:
-------------------------
     attribute_name       = COL_N
     attribute_subname    = ATT1
     expression           = log(10, VALUE)
     reverse_expression   = power(10, VALUE)
-------------------------
transformation record #2:
-------------------------
     attribute_name       = null
     attribute_subname    = COL_N
     expression           = ln(VALUE)
     reverse_expression   = exp(VALUE)

After stacking, the transformation list is as follows.

-------------------------
transformation record #1:
-------------------------
     attribute_name       = COL_N
     attribute_subname    = ATT1
     expression           = (log(10, VALUE) - 0)/10
     reverse_expression   = power(10, VALUE*10 + 0)
-------------------------
transformation record #2:
-------------------------
     attribute_name       = NULL
     attribute_subname    = COL_N
     expression           = (ln(VALUE)- 0)/10
     reverse_expression   = exp(VALUE *10 + 0)
-------------------------
transformation record #3:
-------------------------
     attribute_name       = COL_N
     attribute_subname    = ATT2
     expression           = (ln(VALUE) - 0)/20
     reverse_expression   = exp(VALUE * 20 + 0)

Security Model

The DBMS_DATA_MINING_TRANSFORM package is owned by user SYS and is installed as part of database installation. Execution privilege on the package is granted to public. The routines in the package are run with invokers' rights (run with the privileges of the current user).

The DBMS_DATA_MINING_TRANSFORM.INSERT_* procedures have a data_table_name parameter that enables the user to provide the input data for transformation purposes. The value of data_table_name can be the name of a physical table or a view. The data_table_name parameter can also accept an inline query.

Important:

Because an inline query can be used to specify the data for transformation, Oracle strongly recommends that the calling routine perform any necessary SQL injection checks on the input string.

See Also:

"Operational Notes" for a description of the DBMS_DATA_MINING_TRANSFORM.INSERT_* procedures

Types

DBMS_DATA_MINING_TRANSFORM defines the data types described in Table 45-1.

Table 45-1 Data Types in DBMS_DATA_MINING_TRANSFORM

List Type List Elements Description
COLUMN_
LIST
VARRAY(1000) OF varchar2(32)

COLUMN_LIST stores quoted and non-quoted identifiers for column names.

COLUMN_LIST is the data type of the exclude_list parameter in the INSERT procedures. See "INSERT_AUTOBIN_NUM_EQWIDTH Procedure" for an example.

See Oracle Database PL/SQL Language Reference for information about populating VARRAY structures.

DESCRIBE_
LIST
DBMS_SQL.DESC_TAB2

TYPE desc_tab2 IS TABLE OF desc_rec2 
INDEX BY BINARY_INTEGER

TYPE desc_rec2 IS RECORD (
col_type            BINARY_INTEGER := 0,
col_max_len         BINARY_INTEGER := 0,
col_name            VARCHAR2(32767):= '',
col_name_len        BINARY_INTEGER := 0,
col_schema_name     VARCHAR2(32)   := '',
col_schema_name_len BINARY_INTEGER := 0,
col_precision       BINARY_INTEGER := 0,
col_scale           BINARY_INTEGER := 0,
col_charsetid       BINARY_INTEGER := 0,
col_charsetform     BINARY_INTEGER := 0,
col_null_ok         BOOLEAN := TRUE);

DESCRIBE_LIST describes the columns of the data table after the transformation list has been applied. A DESCRIBE_LIST is returned by the DESCRIBE_STACK Procedure.

The DESC_TAB2 and DESC_REC2 types are defined in the DBMS_SQL package. See "DESC_REC2 Record Type".

The col_type field of DESC_REC2 identifies the data type of the column. The data type is expressed as a numeric constant that represents a built-in data type. For example, a 1 indicates a variable length character string. The codes for Oracle built-in data types are listed in Oracle Database SQL Language Reference. The codes for the Oracle Data Mining nested types are described in "Constants".

The col_name field of DESC_REC2 identifies the column name. It may be populated with a column name, an alias, or an expression. If the column name is a SELECT expression, it may be very long. If the expression is longer than 30 bytes, it cannot be used in a view unless it is given an alias.

TRANSFORM_
LIST
TABLE OF transform_rec

TYPE transform_rec IS RECORD (
attribute_name      VARCHAR2(30),
attribute_subname   VARCHAR2(4000),
expression          EXPRESSION_REC,
reverse_expression  EXPRESSION_REC,
attribute_spec      VARCHAR2(4000));

TYPE expression_rec IS RECORD (
lstmt       DBMS_SQL.VARCHAR2A,
lb          BINARY_INTEGER DEFAULT 1,
ub          BINARY_INTEGER DEFAULT 0);

TYPE varchar2a IS TABLE OF VARCHAR2(32767)
INDEX BY BINARY_INTEGER;

TRANSFORM_LIST is a list of transformations that can be embedded in a model. A TRANSFORM_LIST is accepted as an argument by the CREATE_MODEL Procedure.

Each element in a TRANSFORM_LIST is a TRANSFORM_REC that specifies how to transform a single attribute. The attribute_name is a column name. The attribute_subname is the nested attribute name if the column is nested, otherwise attribute_subname is null.

The expression field holds a SQL expression for transforming the attribute. See "About Transformation Lists" for an explanation of reverse expressions.

The attribute_spec field can be used to disable ADP for this attribute. Specify the keyword NOPREP to disable ADP. By default attribute_spec is null.

The expressions in a TRANSFORM_REC have type EXPRESSION_REC. The lstmt field stores a VARCHAR2A, which is a table of VARCHAR2(32767. The VARCHAR2A data type allows transformation expressions to be very long, as they can be broken up across multiple rows of VARCHAR2. The VARCHAR2A type is defined in the DBMS_SQL package. See "VARCHAR2A Table Type".

The ub (upper bound) and lb (lower bound) fields indicate how many rows there are in the VARCHAR2A table. If ub < lb (default) the EXPRESSION_REC is empty; if lb=ub=1 there is one row; if lb=1 and ub=2 there are 2 rows, and so on.



Constants

DBMS_DATA_MINING_TRANSFORM defines the constants described in Table 45-2.

Table 45-2 Constants in DBMS_DATA_MINING_TRANSFORM

Constant Value Description

NEST_NUM_COL_TYPE

100001

Indicates that an attribute in the transformation list comes from a row in a column of DM_NESTED_NUMERICALS.

Nested numerical attributes are defined as follows:

attribute_name       VARCHAR2(4000)
value                NUMBER

NEST_CAT_COL_TYPE

100002

Indicates that an attribute in the transformation list comes from a row in a column of DM_NESTED_CATAGORICALS.

Nested categorical attributes are defined as follows:

attribute_name       VARCHAR2(4000)
value                VARCHAR2(4000)

See Also:

Oracle Data Mining Application Developer's Guide for information about nested data in Oracle Data Mining

Summary of DBMS_DATA_MINING_TRANSFORM Subprograms

Table 45-3 DBMS_DATA_MINING_TRANSFORM Package Subprograms

Subprogram Purpose

CREATE_BIN_CAT Procedure

Creates a transformation definition table for categorical binning

CREATE_BIN_NUM Procedure

Creates a transformation definition table for numerical binning

CREATE_CLIP Procedure

Creates a transformation definition table for clipping

CREATE_COL_REM Procedure

Creates a transformation definition table for column removal

CREATE_MISS_CAT Procedure

Creates a transformation definition table for categorical missing value treatment

CREATE_MISS_NUM Procedure

Creates a transformation definition table for numerical missing values treatment

CREATE_NORM_LIN Procedure

Creates a transformation definition table for linear normalization

DESCRIBE_STACK Procedure

Describes the transformation list

GET_EXPRESSION Function

Returns a VARCHAR2 chunk from a transformation expression

INSERT_AUTOBIN_NUM_EQWIDTH Procedure

Inserts numeric automatic equi-width binning definitions in a transformation definition table

INSERT_BIN_CAT_FREQ Procedure

Inserts categorical frequency-based binning definitions in a transformation definition table

INSERT_BIN_NUM_EQWIDTH Procedure

Inserts numeric equi-width binning definitions in a transformation definition table

INSERT_BIN_NUM_QTILE Procedure

Inserts numeric quantile binning expressions in a transformation definition table

INSERT_BIN_SUPER Procedure

Inserts supervised binning definitions in numerical and categorical transformation definition tables

INSERT_CLIP_TRIM_TAIL Procedure

Inserts numerical trimming definitions in a transformation definition table

INSERT_CLIP_WINSOR_TAIL Procedure

Inserts numerical winsorizing definitions in a transformation definition table

INSERT_MISS_CAT_MODE Procedure

Inserts categorical missing value treatment definitions in a transformation definition table

INSERT_MISS_NUM_MEAN Procedure

Inserts numerical missing value treatment definitions in a transformation definition table

INSERT_NORM_LIN_MINMAX Procedure

Inserts linear min-max normalization definitions in a transformation definition table

INSERT_NORM_LIN_SCALE Procedure

Inserts linear scale normalization definitions in a transformation definition table

INSERT_NORM_LIN_ZSCORE Procedure

Inserts linear zscore normalization definitions in a transformation definition table

SET_EXPRESSION Procedure

Adds a VARCHAR2 chunk to an expression

SET_TRANSFORM Procedure

Adds a transformation record to a transformation list

STACK_BIN_CAT Procedure

Adds a categorical binning expression to a transformation list

STACK_BIN_NUM Procedure

Adds a numerical binning expression to a transformation list

STACK_CLIP Procedure

Adds a clipping expression to a transformation list

STACK_COL_REM Procedure

Adds a column removal expression to a transformation list

STACK_MISS_CAT Procedure

Adds a categorical missing value treatment expression to a transformation list

STACK_MISS_NUM Procedure

Adds a numerical missing value treatment expression to a transformation list

STACK_NORM_LIN Procedure

Adds a linear normalization expression to a transformation list

XFORM_BIN_CAT Procedure

Creates a view of the data table with categorical binning transformations

XFORM_BIN_NUM Procedure

Creates a view of the data table with numerical binning transformations

XFORM_CLIP Procedure

Creates a view of the data table with clipping transformations

XFORM_COL_REM Procedure

Creates a view of the data table with column removal transformations

XFORM_EXPR_NUM Procedure

Creates a view of the data table with the specified numeric transformations

XFORM_EXPR_STR Procedure

Creates a view of the data table with the specified categorical transformations

XFORM_MISS_CAT Procedure

Creates a view of the data table with categorical missing value treatment

XFORM_MISS_NUM Procedure

Creates a view of the data table with numerical missing value treatment

XFORM_NORM_LIN Procedure

Creates a view of the data table with linear normalization transformations

XFORM_STACK Procedure

Creates a view of the transformation list



CREATE_BIN_CAT Procedure

This procedure creates a transformation definition table for categorical binning. The columns are described in Table 45-4.

Table 45-4 Columns in a Transformation Definition Table for Categorical Binning

Name Data Type Description

col

VARCHAR2(30)

Name of a column of CHAR, VARCHAR2, or DM_NESTED_CATEGORICALS in the data to be mined.

If col is CHAR or VARCHAR2, the column name is also the attribute name.

For information about attribute names, see Oracle Data Mining Application Developer's Guide. Refer especially to the section, "Scoping of Model Attribute Name".

att

VARCHAR2(4000)

The attribute subname if col is a nested column of DM_NESTED_CATEGORICALS. If col is nested, the attribute name is col.att.

If col is not nested, att is null.

val

VARCHAR2(4000)

Values of the attribute

bin

VARCHAR2(4000)

Bin assignments for the values


Syntax

DBMS_DATA_MINING_TRANSFORM.CREATE_BIN_CAT (
     bin_table_name     IN VARCHAR2,
     bin_schema_name    IN VARCHAR2 DEFAULT NULL );

Parameters

Table 45-5 CREATE_BIN_CAT Procedure Parameters

Parameter Description

bin_table_name

Name of the transformation definition table to be created

bin_schema_name

Schema of bin_table_name. If no schema is specified, the current schema is used.


Usage Notes

You can use the following procedures to populate the transformation definition table:

Examples

The following statement creates a table called bin_cat_xtbl in the current schema. The table has columns that can be populated with bin assignments for categorical attributes.

BEGIN
   DBMS_DATA_MINING_TRANSFORM.CREATE_BIN_CAT('bin_cat_xtbl');
END;
/
DESCRIBE bin_cat_xtbl
 Name                                      Null?    Type
 ----------------------------------------- -------- ----------------------------
 COL                                                VARCHAR2(30)
 ATT                                                VARCHAR2(4000)
 VAL                                                VARCHAR2(4000)
 BIN                                                VARCHAR2(4000)

CREATE_BIN_NUM Procedure

This procedure creates a transformation definition table for numerical binning. The columns are described in Table 45-6.

Table 45-6 Columns in a Transformation Definition Table for Numerical Binning

Name Data Type Description

col

VARCHAR2(30)

Name of a column of NUMBER, FLOAT, or DM_NESTED_NUMERICALS in the data to be mined.

If the column is NUMBER or FLOAT, the column name is also the attribute name.

For information about attribute names, see Oracle Data Mining Application Developer's Guide. Refer especially to the section, "Scoping of Model Attribute Name".

att

VARCHAR2(4000)

The attribute subname if col is a nested column of DM_NESTED_NUMERICALS. If col is nested, the attribute name is col.att.

If col is not nested, att is null.

val

NUMBER

Values of the attribute

bin

VARCHAR2(4000)

Bin assignments for the values


Syntax

DBMS_DATA_MINING_TRANSFORM.CREATE_BIN_NUM (
     bin_table_name    IN VARCHAR2,
     bin_schema_name   IN VARCHAR2 DEFAULT NULL );

Parameters

Table 45-7 CREATE_BIN_NUM Procedure Parameters

Parameter Description

bin_table_name

Name of the transformation definition table to be created

bin_schema_name

Schema of bin_table_name. If no schema is specified, the current schema is used.


Usage Notes

You can use the following procedures to populate the transformation definition table:

Examples

The following statement creates a table called bin_num_xtbl in the current schema. The table has columns that can be populated with bin assignments for numerical attributes.

BEGIN
  DBMS_DATA_MINING_TRANSFORM.CREATE_BIN_NUM('bin_num_xtbl');
END;
/
 
DESCRIBE bin_num_xtbl
 Name                                      Null?    Type
 ----------------------------------------- -------- ----------------------------
 COL                                                VARCHAR2(30)
 ATT                                                VARCHAR2(4000)
 VAL                                                NUMBER
 BIN                                                VARCHAR2(4000)

CREATE_CLIP Procedure

This procedure creates a transformation definition table for clipping or winsorizing to minimize the effect of outliers. The columns are described in Table 45-8.

Table 45-8 Columns in a Transformation Definition Table for Clipping or Winsorizing

Name Data Type Description

col

VARCHAR2(30)

Name of a column of NUMBER, FLOAT, or DM_NESTED_NUMERICALS in the data to be mined.

If col is NUMBER or FLOAT, the column name is also the attribute name.

For information about attribute names, see Oracle Data Mining Application Developer's Guide. Refer especially to the section, "Scoping of Model Attribute Name".

att

VARCHAR2(4000)

The attribute subname if col is a nested column of DM_NESTED_NUMERICALS. If col is nested, the attribute name is col.att.

If col is not nested, att is null.

lcut

NUMBER

The lowest typical value for the attribute.

If the attribute values were plotted on an xy axis, lcut would be the left-most boundary of the range of values considered typical for this attribute.

Any values to the left of lcut are outliers.

lval

NUMBER

Value assigned to an outlier to the left of lcut

rcut

NUMBER

The highest typical value for the attribute

If the attribute values were plotted on an xy axis, rcut would be the right-most boundary of the range of values considered typical for this attribute.

Any values to the right of rcut are outliers.

rval

NUMBER

Value assigned to an outlier to the right of rcut


Syntax

DBMS_DATA_MINING_TRANSFORM.CREATE_CLIP (
     clip_table_name    IN VARCHAR2,
     clip_schema_name   IN VARCHAR2 DEFAULT NULL );

Parameters

Table 45-9 CREATE_CLIP Procedure Parameters

Parameter Description

clip_table_name

Name of the transformation definition table to be created

clip_schema_name

Schema of clip_table_name. If no schema is specified, the current schema is used.


Usage Notes

You can use the following procedures to populate the transformation definition table:

Examples

The following statement creates a table called clip_xtbl in the current schema. The table has columns that can be populated with clipping instructions for numerical attributes.

BEGIN
  DBMS_DATA_MINING_TRANSFORM.CREATE_CLIP('clip_xtbl');
END;
/
 
DESCRIBE clip_xtbl
 Name                                      Null?    Type
 ----------------------------------------- -------- ----------------------------
 COL                                                VARCHAR2(30)
 ATT                                                VARCHAR2(4000)
 LCUT                                               NUMBER
 LVAL                                               NUMBER
 RCUT                                               NUMBER
 RVAL                                               NUMBER

CREATE_COL_REM Procedure

This procedure creates a transformation definition table for removing columns from the data table. The columns are described in Table 45-10.

Table 45-10 Columns in a Transformation Definition Table for Column Removal

Name Data Type Description

col

VARCHAR2(30)

Name of a column in the data to be mined. If col is not nested, the column name is also the attribute name.

For information about attribute names, see Oracle Data Mining Application Developer's Guide. Refer especially to the section, "Scoping of Model Attribute Name".

att

VARCHAR2(4000)

The attribute subname if col is nested (DM_NESTED_NUMERICALS or DM_NESTED_CATEGORICALS). If col is nested, the attribute name is col.att.

If col is not nested, att is null.


Syntax

DBMS_DATA_MINING_TRANSFORM.CREATE_COL_REM (
      rem_table_name           VARCHAR2,
      rem_schema_name          VARCHAR2 DEFAULT NULL );

Parameters

Table 45-11 CREATE_COL_REM Procedure Parameters

Parameter Description

rem_table_name

Name of the transformation definition table to be created

rem_schema_name

Schema of rem_table_name. If no schema is specified, the current schema is used.


Usage Notes

See "Operational Notes".

Examples

The following statement creates a table called rem_att_xtbl in the current schema. The table has columns that can be populated with the names of attributes to exclude from the data to be mined.

BEGIN
    DBMS_DATA_MINING_TRANSFORM.CREATE_COL_REM ('rem_att_xtbl');
END;
 /
DESCRIBE rem_att_xtbl
 Name                                      Null?    Type
 ----------------------------------------- -------- ----------------------------
 COL                                                VARCHAR2(30)
 ATT                                                VARCHAR2(4000)

CREATE_MISS_CAT Procedure

This procedure creates a transformation definition table for replacing categorical missing values. The columns are described in Table 45-12.

Table 45-12 Columns in a Transformation Definition Table for Categorical Missing Value Treatment

Name Data Type Description

col

VARCHAR2(30)

Name of a column of CHAR, VARCHAR2, or DM_NESTED_CATEGORICALS in the data to be mined.

If col is CHAR or VARCHAR2, the column name is also the attribute name.

For information about attribute names, see Oracle Data Mining Application Developer's Guide. Refer especially to the section, "Scoping of Model Attribute Name".

att

VARCHAR2(4000)

The attribute subname if col is a nested column of DM_NESTED_CATEGORICALS. If col is nested, the attribute name is col.att.

If col is not nested, att is null.

val

VARCHAR2(4000)

Replacement for missing values in the attribute


Syntax

DBMS_DATA_MINING_TRANSFORM.CREATE_MISS_CAT (
     miss_table_name       IN VARCHAR2,
     miss_schema_name      IN VARCHAR2 DEFAULT NULL );

Parameters

Table 45-13 CREATE_MISS_CAT Procedure Parameters

Parameter Description

miss_table_name

Name of the transformation definition table to be created

miss_schema_name

Schema of miss_table_name. If no schema is specified, the current schema is used.


Usage Notes

You can use the INSERT_MISS_CAT_MODE Procedure to populate the transformation definition table.

Examples

The following statement creates a table called miss_cat_xtbl in the current schema. The table has columns that can be populated with values for missing data in categorical attributes.

BEGIN
DBMS_DATA_MINING_TRANSFORM.CREATE_MISS_CAT('miss_cat_xtbl');
END;
/
 
DESCRIBE miss_cat_xtbl
 Name                                      Null?    Type
 ----------------------------------------- -------- ----------------------------
 COL                                                VARCHAR2(30)
 ATT                                                VARCHAR2(4000)
 VAL                                                VARCHAR2(4000)

CREATE_MISS_NUM Procedure

This procedure creates a transformation definition table for replacing numerical missing values. The columns are described in Table 45-14.

Table 45-14 Columns in a Transformation Definition Table for Numerical Missing Value Treatment

Name Data Type Description

col

VARCHAR2(30)

Name of a column of NUMBER, FLOAT, or DM_NESTED_NUMERICALS in the data to be mined.

If the column is NUMBER or FLOAT, the column name is also the attribute name.

For information about attribute names, see Oracle Data Mining Application Developer's Guide. Refer especially to the section, "Scoping of Model Attribute Name".

att

VARCHAR2(4000)

The attribute subname if col is a nested column of DM_NESTED_NUMERICALS. If col is nested, the attribute name is col.att.

If col is not nested, att is null.

val

NUMBER

Replacement for missing values in the attribute


Syntax

DBMS_DATA_MINING_TRANSFORM.CREATE_MISS_NUM (
     miss_table_name       IN VARCHAR2,
     miss_schema_name      IN VARCHAR2 DEFAULT NULL );

Parameters

Table 45-15 CREATE_MISS_NUM Procedure Parameters

Parameter Description

miss_table_name

Name of the transformation definition table to be created

miss_schema_name

Schema of miss_table_name. If no schema is specified, the current schema is used.


Usage Notes

You can use the INSERT_MISS_NUM_MEAN Procedure to populate the transformation definition table.

Example

The following statement creates a table called miss_num_xtbl in the current schema. The table has columns that can be populated with values for missing data in numerical attributes.

BEGIN
    DBMS_DATA_MINING_TRANSFORM.CREATE_MISS_NUM('miss_num_xtbl');
END;
/
 
DESCRIBE miss_num_xtbl
 Name                                      Null?    Type
 ----------------------------------------- -------- ----------------------------
 COL                                                VARCHAR2(30)
 ATT                                                VARCHAR2(4000)
 VAL                                                NUMBER

CREATE_NORM_LIN Procedure

This procedure creates a transformation definition table for linear normalization. The columns are described in Table 45-16.

Table 45-16 Columns in a Transformation Definition Table for Linear Normalization

Name Data Type Description

col

VARCHAR2(30)

Name of a column of NUMBER, FLOAT, or DM_NESTED_NUMERICALS in the data to be mined.

If the column is NUMBER or FLOAT, the column name is also the attribute name.

For information about attribute names, see Oracle Data Mining Application Developer's Guide. Refer especially to the section, "Scoping of Model Attribute Name".

att

VARCHAR2(4000)

The attribute subname if col is a nested column of DM_NESTED_NUMERICALS. If col is nested, the attribute name is col.att.

If col is not nested, att is null.

shift

NUMBER

A constant to subtract from the attribute values

scale

NUMBER

A constant by which to divide the shifted values


Syntax

DBMS_DATA_MINING_TRANSFORM.CREATE_NORM_LIN (
     norm_table_name       IN VARCHAR2,
     norm_schema_name      IN VARCHAR2 DEFAULT NULL );

Parameters

Table 45-17 CREATE_NORM_LIN Procedure Parameters

Parameter Description

norm_table_name

Name of the transformation definition table to be created

norm_schema_name

Schema of norm_table_name. If no schema is specified, the current schema is used.


Usage Notes

You can use the following procedures to populate the transformation definition table:

Examples

The following statement creates a table called norm_xtbl in the current schema. The table has columns that can be populated with shift and scale values for normalizing numerical attributes.

BEGIN
     DBMS_DATA_MINING_TRANSFORM.CREATE_NORM_LIN('norm_xtbl');
END;
/
 
DESCRIBE norm_xtbl
 Name                                      Null?    Type
 ----------------------------------------- -------- ----------------------------
 COL                                                VARCHAR2(30)
 ATT                                                VARCHAR2(4000)
 SHIFT                                              NUMBER
 SCALE                                              NUMBER

DESCRIBE_STACK Procedure

This procedure describes the columns of the data table after a list of transformations has been applied.Only the columns that are specified in the transformation list are transformed. The remaining columns in the data table are included in the output without changes.

To create a view of the data table after the transformations have been applied, use the XFORM_STACK Procedure.

Syntax

DBMS_DATA_MINING_TRANSFORM.DESCRIBE_STACK (
     xform_list           IN  TRANSFORM_LIST,
     data_table_name      IN  VARCHAR2,
     describe_list        OUT DESCRIBE_LIST,
     data_schema_name     IN  VARCHAR2 DEFAULT NULL);

Parameters

Table 45-18 DESCRIBE_STACK Procedure Parameters

Parameter Description

xform_list

A list of transformations. See Table 45-1 for a description of the TRANSFORM_LIST object type.

data_table_name

Name of the table containing the data to be transformed

describe_list

Descriptions of the columns in the data table after the transformations specified in xform_list have been applied. See Table 45-1 for a description of the DESCRIBE_LIST object type.

data_schema_name

Schema of data_table_name. If no schema is specified, the current schema is used.


Usage Notes

See "Operational Notes" for information about transformation lists and embedded transformations.

Examples

This example shows the column name and data type, the column name length, and the column maximum length for the view dmuser.cust_info after the transformation list has been applied. All the transformations are user-specified. The results of DESCRIBE_STACK do not include one of the columns in the original table, because the SET_TRANSFORM procedure sets that column to NULL.

CREATE OR REPLACE VIEW cust_info AS
         SELECT a.cust_id, c.country_id, c.cust_year_of_birth,
         CAST(COLLECT(DM_Nested_Numerical(
                  b.prod_name, 1))
                AS DM_Nested_Numericals) custprods
                 FROM sh.sales a, sh.products b, sh.customers c
                  WHERE a.prod_id = b.prod_id AND
                        a.cust_id=c.cust_id and
                        a.cust_id between 100001 AND 105000
         GROUP BY a.cust_id, country_id, cust_year_of_birth;
 
describe cust_info
 Name                                      Null?    Type
 ----------------------------------------- -------- ----------------------------
 CUST_ID                                   NOT NULL NUMBER
 COUNTRY_ID                                NOT NULL NUMBER
 CUST_YEAR_OF_BIRTH                        NOT NULL NUMBER(4)
 CUSTPRODS                                          SYS.DM_NESTED_NUMERICALS
 
DECLARE
  cust_stack   dbms_data_mining_transform.TRANSFORM_LIST;
  cust_cols    dbms_data_mining_transform.DESCRIBE_LIST;
BEGIN
  dbms_data_mining_transform.SET_TRANSFORM (cust_stack,
     'country_id', NULL, 'country_id/10', 'country_id*10');
  dbms_data_mining_transform.SET_TRANSFORM (cust_stack,
      'cust_year_of_birth', NULL, NULL, NULL);
  dbms_data_mining_transform.SET_TRANSFORM (cust_stack,
      'custprods', 'Mouse Pad', 'value*100', 'value/100');
  dbms_data_mining_transform.DESCRIBE_STACK(
       xform_list => cust_stack,
       data_table_name => 'cust_info',
       describe_list => cust_cols);
  dbms_output.put_line('====');
  for i in 1..cust_cols.COUNT loop
    dbms_output.put_line('COLUMN_NAME:     '||cust_cols(i).col_name);
    dbms_output.put_line('COLUMN_TYPE:     '||cust_cols(i).col_type);
    dbms_output.put_line('COLUMN_NAME_LEN: '||cust_cols(i).col_name_len);
    dbms_output.put_line('COLUMN_MAX_LEN:  '||cust_cols(i).col_max_len);
    dbms_output.put_line('====');
  END loop;
END;
/
====
COLUMN_NAME:     CUST_ID
COLUMN_TYPE:     2
COLUMN_NAME_LEN: 7
COLUMN_MAX_LEN:  22
====
COLUMN_NAME:     COUNTRY_ID
COLUMN_TYPE:     2
COLUMN_NAME_LEN: 10
COLUMN_MAX_LEN:  22
====
COLUMN_NAME:     CUSTPRODS
COLUMN_TYPE:     100001
COLUMN_NAME_LEN: 9
COLUMN_MAX_LEN:  40
====

GET_EXPRESSION Function

This function returns a row from a VARCHAR2 array that stores a transformation expression. The array is built by calls to the SET_EXPRESSION Procedure.

The array can be used for specifying SQL expressions that are too long to be used with the SET_TRANSFORM Procedure.

Syntax

DBMS_DATA_MINING_TRANSFORM.GET_EXPRESSION (
     expression           IN EXPRESSION_REC,
     chunk_num            IN PLS_INTEGER DEFAULT NULL);
 RETURN VARCHAR2;

Parameters

Table 45-19 GET_EXPRESSION Function Parameters

Parameter Description

expression

An expression record (EXPRESSION_REC) that specifies a transformation expression or a reverse transformation expression for an attribute. Each expression record includes a VARCHAR2 array and index fields for specifying upper and lower boundaries within the array.

There are two EXPRESSION_REC fields within a transformation record (TRANSFORM_REC): one for the transformation expression; the other for the reverse transformation expression.

See Table 45-1 for a description of the EXPRESSION_REC type.

chunk

A VARCHAR2 chunk (row) to be appended to expression.


Usage Notes

  1. Chunk numbering starts with one. For chunks outside of the range, the return value is null. When a chunk number is null the whole expression is returned as a string. If the expression is too big, a VALUE_ERROR is raised.

  2. See "About Transformation Lists".

  3. See "Operational Notes".

Examples

See the example for the SET_EXPRESSION Procedure.


INSERT_AUTOBIN_NUM_EQWIDTH Procedure

This procedure performs numerical binning and inserts the transformation definitions in a transformation definition table. The procedure identifies the minimum and maximum values and computes the bin boundaries at equal intervals.

INSERT_AUTOBIN_NUM_EQWIDTH computes the number of bins separately for each column. If you want to use equi-width binning with the same number of bins for each column, use the INSERT_BIN_NUM_EQWIDTH Procedure.

INSERT_AUTOBIN_NUM_EQWIDTH bins all the NUMBER and FLOAT columns in the data source unless you specify a list of columns to ignore.

Syntax

DBMS_DATA_MINING_TRANSFORM.INSERT_AUTOBIN_NUM_EQWIDTH (
     bin_table_name        IN VARCHAR2,
     data_table_name       IN VARCHAR2,
     bin_num               IN PLS_INTEGER DEFAULT 3,
     max_bin_num           IN PLS_INTEGER DEFAULT 100,
     exclude_list          IN COLUMN_LIST DEFAULT NULL,
     round_num             IN PLS_INTEGER DEFAULT 6,
     sample_size           IN PLS_INTEGER DEFAULT 50000,
     bin_schema_name       IN VARCHAR2 DEFAULT NULL,
     data_schema_name      IN VARCHAR2 DEFAULT NULL,
     rem_table_name        IN VARCHAR2 DEFAULT NULL,
     rem_schema_name       IN VARCHAR2 DEFAULT NULL));

Parameters

Table 45-20 INSERT_AUTOBIN_NUM_EQWIDTH Procedure Parameters

Parameter Description

bin_table_name

Name of the transformation definition table for numerical binning. You can use the CREATE_BIN_NUM Procedure to create the definition table. The following columns are required:

COL       VARCHAR2(30)
VAL       NUMBER
BIN       VARCHAR2(4000)

CREATE_BIN_NUM creates an additional column, ATT, which may be used for specifying nested attributes. This column is not used by INSERT_AUTOBIN_NUM_EQWIDTH.

data_table_name

Name of the table containing the data to be transformed

bin_num

Minimum number of bins. If bin_num is 0 or NULL, it is ignored.

The default value of bin_num is 3.

max_bin_num

Maximum number of bins. If max_bin_num is 0 or NULL, it is ignored.

The default value of max_bin_num is 100.

exclude_list

List of numeric columns to be excluded from the binning process. If you do not specify exclude_list, all numeric columns in the data source are binned.

The format of exclude_list is:

dbms_data_mining_transform.COLUMN_LIST('col1','col2',
                                              ...'coln')

round_num

Specifies how to round the number in the VAL column of the transformation definition table.

When round_num is positive, it specifies the most significant digits to retain. When round_num is negative, it specifies the least significant digits to remove. In both cases, the result is rounded to the specified number of digits. See the Usage Notes for an example.

The default value of round_num is 6.

sample_size

Size of the data sample. If sample_size is less than the total number of non-NULL values in the column, then sample_size is used instead of the SQL COUNT function in computing the number of bins. If sample_size is 0 or NULL, it is ignored. See the Usage Notes.

The default value of sample_size is 50,000.

bin_schema_name

Schema of bin_table_name. If no schema is specified, the current schema is used.

data_schema_name

Schema of data_table_name. If no schema is specified, the current schema is used.

rem_table_name

Name of a transformation definition table for column removal. The table must have the columns described in "CREATE_COL_REM Procedure".

INSERT_AUTOBIN_NUM_EQWIDTH ignores columns with all nulls or only one unique value. If you specify a value for rem_table_name, these columns are removed from the mining data. If you do not specify a value for rem_table_name, these unbinned columns remain in the data.

rem_schema_name

Schema of rem_table_name. If no schema is specified, the current schema is used.


Usage Notes

  1. INSERT_AUTOBIN_NUM_EQWIDTH computes the number of bins for a column based on the number of non-null values (COUNT), the maximum (MAX), the minimum (MIN), the standard deviation (STDDEV), and the constant C=3.49/0.9:

    N=floor(power(COUNT,1/3)*(max-min)/(c*dev))
    

    If the sample_size parameter is specified, it is used instead of COUNT.

    See Oracle Database SQL Language Reference for information about the COUNT, MAX, MIN, STDDEV, FLOOR, and POWER functions.

  2. INSERT_AUTOBIN_NUM_EQWIDTH uses absolute values to compute the number of bins. The sign of the parameters bin_num, max_bin_num, and sample_size has no effect on the result.

  3. In computing the number of bins, INSERT_AUTOBIN_NUM_EQWIDTH evaluates the following criteria in the following order:

    1. The minimum number of bins (bin_num)

    2. The maximum number of bins (max_bin_num)

    3. The maximum number of bins for integer columns, calculated as the number of distinct values in the range max-min+1.

  4. The round_num parameter controls the rounding of column values in the transformation definition table, as follows:

    For a value of 308.162:
    when round_num =  1       result is 300
    when round_num =  2       result is 310
    when round_num =  3       result is 308
    when round_num =  0       result is 308.162
    when round_num = -1       result is 308.16
    when round_num = -2       result is 308.2
    

Examples

In this example, INSERT_AUTOBIN_NUM_EQWIDTH computes the bin boundaries for the cust_year_of_birth column in sh.customers and inserts the transformations in a transformation definition table. The STACK_BIN_NUM Procedure creates a transformation list from the contents of the definition table. The CREATE_MODEL Procedure embeds the transformation list in a new model called nb_model.

The transformation and reverse transformation expressions embedded in nb_model are returned by the GET_MODEL_TRANSFORMATIONS Function.

CREATE OR REPLACE VIEW mining_data AS
       SELECT cust_id, cust_year_of_birth, cust_postal_code
       FROM sh.customers;
 
DESCRIBE mining_data
 Name                          Null?    Type
 ----------------------------- -------- ----------------------------
 CUST_ID                       NOT NULL NUMBER
 CUST_YEAR_OF_BIRTH            NOT NULL NUMBER(4)
 CUST_POSTAL_CODE              NOT NULL VARCHAR2(10)
 
BEGIN
  dbms_data_mining_transform.CREATE_BIN_NUM(
     bin_table_name   => 'bin_tbl');
  dbms_data_mining_transform.INSERT_AUTOBIN_NUM_EQWIDTH (
     bin_table_name   => 'bin_tbl',
     data_table_name  => 'mining_data',
     bin_num          => 3,
     max_bin_num      => 5,
     exclude_list     => dbms_data_mining_transform.COLUMN_LIST('cust_id'));
END;
/

set numwidth 4
column val off
SELECT col, val, bin FROM bin_tbl
      ORDER BY val ASC;
 
COL                        VAL BIN
------------------------- ---- -----
CUST_YEAR_OF_BIRTH        1913
CUST_YEAR_OF_BIRTH        1928 1
CUST_YEAR_OF_BIRTH        1944 2
CUST_YEAR_OF_BIRTH        1959 3
CUST_YEAR_OF_BIRTH        1975 4
CUST_YEAR_OF_BIRTH        1990 5

DECLARE
     year_birth_xform   dbms_data_mining_transform.TRANSFORM_LIST;
BEGIN
     dbms_data_mining_transform.STACK_BIN_NUM (
         bin_table_name           => 'bin_tbl',
         xform_list               =>  year_birth_xform);
     dbms_data_mining.CREATE_MODEL(
         model_name              => 'nb_model',
         mining_function         => dbms_data_mining.classification,
         data_table_name         => 'mining_data',
         case_id_column_name     => 'cust_id',
         target_column_name      => 'cust_postal_code',
         settings_table_name     => null,
         data_schema_name        => null,
         settings_schema_name    => null,
         xform_list              => year_birth_xform);
END;
/
 
SELECT attribute_name 
       FROM TABLE(dbms_data_mining.GET_MODEL_TRANSFORMATIONS('nb_model'));
 
ATTRIBUTE_NAME
------------------------
CUST_YEAR_OF_BIRTH
 
SELECT expression 
       FROM TABLE(dbms_data_mining.GET_MODEL_TRANSFORMATIONS('nb_model'));
 
EXPRESSION
--------------------------------------------------------------------------------
CASE WHEN "CUST_YEAR_OF_BIRTH"<1913 THEN NULL WHEN "CUST_YEAR_OF_BIRTH"<=1928.4
 THEN '1' WHEN "CUST_YEAR_OF_BIRTH"<=1943.8 THEN '2' WHEN "CUST_YEAR_OF_BIRTH"
<=1959.2 THEN '3' WHEN "CUST_YEAR_OF_BIRTH"<=1974.6 THEN '4' WHEN
"CUST_YEAR_OF_BIRTH" <=1990 THEN '5' END
 
SELECT reverse_expression 
       FROM TABLE(dbms_data_mining.GET_MODEL_TRANSFORMATIONS('nb_model'));
 
REVERSE_EXPRESSION
--------------------------------------------------------------------------------
DECODE("CUST_YEAR_OF_BIRTH",'5','(1974.6; 1990]','1','[1913; 1928.4]','2','(1928
.4; 1943.8]','3','(1943.8; 1959.2]','4','(1959.2; 1974.6]',NULL,'( ; 1913), (199
0;  ), NULL')

INSERT_BIN_CAT_FREQ Procedure

This procedure performs categorical binning and inserts the transformation definitions in a transformation definition table. The procedure computes the bin boundaries based on frequency.

INSERT_BIN_CAT_FREQ bins all the CHAR and VARCHAR2 columns in the data source unless you specify a list of columns to ignore.

Syntax

DBMS_DATA_MINING_TRANSFORM.INSERT_BIN_CAT_FREQ (
     bin_table_name       IN VARCHAR2,
     data_table_name      IN VARCHAR2,
     bin_num              IN PLS_INTEGER DEFAULT 9,
     exclude_list         IN COLUMN_LIST DEFAULT NULL,
     default_num          IN PLS_INTEGER DEFAULT 2,
     bin_support          IN NUMBER DEFAULT NULL,
     bin_schema_name      IN VARCHAR2 DEFAULT NULL,
     data_schema_name     IN VARCHAR2 DEFAULT NULL);

Parameters

Table 45-21 INSERT_BIN_CAT_FREQ Procedure Parameters

Parameter Description

bin_table_name

Name of the transformation definition table for categorical binning. You can use the CREATE_BIN_CAT Procedure to create the definition table.The following columns are required:

COL       VARCHAR2(30)
VAL       VARCHAR2(4000)
BIN       VARCHAR2(4000)

CREATE_BIN_CAT creates an additional column, ATT, which may be used for specifying nested attributes. This column is not used by INSERT_BIN_CAT_FREQ.

data_table_name

Name of the table containing the data to be transformed

bin_num

The number of bins to fill using frequency-based binning The total number of bins will be bin_num+1. The additional bin is the default bin. Classes that are not assigned to a frequency-based bin will be assigned to the default bin.

The default binning order is from highest to lowest: the most frequently occurring class is assigned to the first bin, the second most frequently occurring class is assigned to the second bin, and so on.You can reverse the binning order by specifying a negative number for bin_num. The negative sign causes the binning order to be from lowest to highest.

If the total number of distinct values (classes) in the column is less than bin_num, then a separate bin will be created for each value and the default bin will be empty.

If you specify NULL or 0 for bin_num, no binning is performed.

The default value of bin_num is 9.

exclude_list

List of CHAR and VARCHAR2 columns to be excluded from the binning process. If you do not specify exclude_list, all CHAR and VARCHAR2 columns in the data source are binned.

The format of exclude_list is:

dbms_data_mining_transform.COLUMN_LIST('col1','col2',
                                       ...'coln')

default_num

The number of class occurrences (rows of the same class) required for assignment to the default bin

By default, default_num is the minimum number of occurrences required for assignment to the default bin. For example, if default_num is 3 and a given class occurs only once, it will not be assigned to the default bin. You can change the occurrence requirement from minimum to maximum by specifying a negative number for default_num. For example, if default_num is -3 and a given class occurs only once, it will be assigned to the default bin, but a class that occurs four or more times will not be included.

If you specify NULL or 0 for default_bin, there are no requirements for assignment to the default bin.

The default value of default_num is 2.

bin_support

The number of class occurrences (rows of the same class) required for assignment to a frequency-based bin. bin_support is expressed as a fraction of the total number of rows.

By default, bin_support is the minimum percentage required for assignment to a frequency-based bin. For example, if there are twenty rows of data and you specify.2 for bin_support, then there must be four or more occurrences of a class (.2*20) in order for it to be assigned to a frequency-based bin. You can change bin_support from a minimum percentage to a maximum percentage by specifying a negative number for bin_support. For example, if there are twenty rows of data and you specify -.2 for bin_support, then there must be four or less occurrences of a class in order for it to be assigned to a frequency-based bin.

Classes that occur less than a positive bin_support or more than a negative bin_support will be assigned to the default bin.

If you specify NULL or 0 for bin_support, then there is no support requirement for frequency-based binning.

The default value of bin_support is NULL.

bin_schema_name

Schema of bin_table_name. If no schema is specified, the current schema is used.

data_schema_name

Schema of data_table_name. If no schema is specified, the current schema is used.


Usage Notes

If values occur with the same frequency, INSERT_BIN_CAT_FREQ assigns them in descending order when binning is from most to least frequent, or in ascending order when binning is from least to most frequent.

Examples

  1. In this example, INSERT_BIN_CAT_FREQ computes the bin boundaries for the cust_postal_code and cust_city columns in sh.customers and inserts the transformations in a transformation definition table. The STACK_BIN_CAT Procedure creates a transformation list from the contents of the definition table, and the CREATE_MODEL Procedure embeds the transformation list in a new model called nb_model.

    The transformation and reverse transformation expressions embedded in nb_model are returned by the GET_MODEL_TRANSFORMATIONS Function.

    CREATE OR REPLACE VIEW mining_data AS
              SELECT cust_id, cust_year_of_birth, cust_postal_code, cust_city
              FROM sh.customers;
     
    DESCRIBE mining_data
     Name                                  Null?    Type
     ------------------------------------- -------- -----------------------------
     CUST_ID                               NOT NULL NUMBER
     CUST_YEAR_OF_BIRTH                    NOT NULL NUMBER(4)
     CUST_POSTAL_CODE                      NOT NULL VARCHAR2(10)
     CUST_CITY                             NOT NULL VARCHAR2(30)
     
    BEGIN
        dbms_data_mining_transform.CREATE_BIN_CAT(
           bin_table_name   => 'bin_tbl_1');
        dbms_data_mining_transform.INSERT_BIN_CAT_FREQ (
           bin_table_name   => 'bin_tbl_1',
           data_table_name  => 'mining_data',
           bin_num          => 4);
    END;
    /
     
    column col format a18
    column val format a15
    column bin format a10
    SELECT col, val, bin 
           FROM bin_tbl_1
           ORDER BY col ASC, bin ASC;
     
    COL                VAL             BIN
    ------------------ --------------- ----------
    CUST_CITY          Los Angeles     1
    CUST_CITY          Greenwich       2
    CUST_CITY          Killarney       3
    CUST_CITY          Montara         4
    CUST_CITY                          5
    CUST_POSTAL_CODE   38082           1
    CUST_POSTAL_CODE   63736           2
    CUST_POSTAL_CODE   55787           3
    CUST_POSTAL_CODE   78558           4
    CUST_POSTAL_CODE                   5
     
    DECLARE
          city_xform   dbms_data_mining_transform.TRANSFORM_LIST;
    BEGIN
          dbms_data_mining_transform.STACK_BIN_CAT (
              bin_table_name           => 'bin_tbl_1',
              xform_list               =>  city_xform);
          dbms_data_mining.CREATE_MODEL(
               model_name              => 'nb_model',
               mining_function         => dbms_data_mining.classification,
               data_table_name         => 'mining_data',
               case_id_column_name     => 'cust_id',
               target_column_name      => 'cust_city',
               settings_table_name     => null,
               data_schema_name        => null,
               settings_schema_name    => null,
               xform_list              => city_xform);
    END;
    /
     
    SELECT attribute_name 
           FROM TABLE(dbms_data_mining.GET_MODEL_TRANSFORMATIONS('nb_model'));
     
    ATTRIBUTE_NAME
    -----------------------------------------------------------------------------
    CUST_CITY
    CUST_POSTAL_CODE
     
    SELECT expression 
           FROM TABLE(dbms_data_mining.GET_MODEL_TRANSFORMATIONS('nb_model'));
     
    EXPRESSION
    -----------------------------------------------------------------------------
    DECODE("CUST_CITY",'Greenwich','2','Killarney','3','Los Angeles','1',
    'Montara','4',NULL,NULL,'5')
    DECODE("CUST_POSTAL_CODE",'38082','1','55787','3','63736','2','78558','4',NULL,NULL,'5')
    
    SELECT reverse_expression 
           FROM TABLE(dbms_data_mining.GET_MODEL_TRANSFORMATIONS('nb_model'));
    
    REVERSE_EXPRESSION
    -----------------------------------------------------------------------------
    DECODE("CUST_CITY",'2','''Greenwich''','3','''Killarney''','1',
    '''Los Angeles''','4','''Montara''',NULL,'NULL','5','DEFAULT')
    DECODE("CUST_POSTAL_CODE",'1','''38082''','3','''55787''','2','''63736''',
    '4','''78558''',NULL,'NULL','5','DEFAULT')
    
  2. The binning order in example 1 is from most frequent to least frequent. The following example shows reverse order binning (least frequent to most frequent). The binning order is reversed by setting bin_num to -4 instead of 4.

    BEGIN
        dbms_data_mining_transform.CREATE_BIN_CAT(
            bin_table_name   => 'bin_tbl_reverse');
        dbms_data_mining_transform.INSERT_BIN_CAT_FREQ (
            bin_table_name   => 'bin_tbl_reverse',
            data_table_name  => 'mining_data',
            bin_num          => -4);
     END;
     /
     
    column col format a20
    SELECT col, val, bin 
           FROM bin_tbl_reverse
           ORDER BY col ASC, bin ASC;
     
    COL                  VAL             BIN
    -------------------- --------------- ----------
    CUST_CITY            Tokyo           1
    CUST_CITY            Sliedrecht      2
    CUST_CITY            Haarlem         3
    CUST_CITY            Diemen          4
    CUST_CITY                            5
    CUST_POSTAL_CODE     49358           1
    CUST_POSTAL_CODE     80563           2
    CUST_POSTAL_CODE     74903           3
    CUST_POSTAL_CODE     71349           4
    CUST_POSTAL_CODE                     5
    

INSERT_BIN_NUM_EQWIDTH Procedure

This procedure performs numerical binning and inserts the transformation definitions in a transformation definition table. The procedure identifies the minimum and maximum values and computes the bin boundaries at equal intervals.

INSERT_BIN_NUM_EQWIDTH computes a specified number of bins (n) and assigns (max-min)/n values to each bin. The number of bins is the same for each column. If you want to use equi-width binning, but you want the number of bins to be calculated on a per-column basis, use the INSERT_AUTOBIN_NUM_EQWIDTH Procedure.

INSERT_BIN_NUM_EQWIDTH bins all the NUMBER and FLOAT columns in the data source unless you specify a list of columns to ignore.

Syntax

DBMS_DATA_MINING_TRANSFORM.INSERT_BIN_NUM_EQWIDTH (
     bin_table_name        IN VARCHAR2,
     data_table_name       IN VARCHAR2,
     bin_num               IN PLS_INTEGER DEFAULT 10,
     exclude_list          IN COLUMN_LIST DEFAULT NULL,
     round_num             IN PLS_INTEGER DEFAULT 6,
     bin_schema_name       IN VARCHAR2 DEFAULT NULL,
     data_schema_name      IN VARCHAR2 DEFAULT NULL);

Parameters

Table 45-22 INSERT_BIN_NUM_EQWIDTH Procedure Parameters

Parameter Description

bin_table_name

Name of the transformation definition table for numerical binning. You can use the CREATE_BIN_NUM Procedure to create the definition table. The following columns are required:

COL       VARCHAR2(30)
VAL       NUMBER
BIN       VARCHAR2(4000)

CREATE_BIN_NUM creates an additional column, ATT, which may be used for specifying nested attributes. This column is not used by INSERT_BIN_NUM_EQWIDTH.

data_table_name

Name of the table containing the data to be transformed

bin_num

Number of bins. No binning occurs if bin_num is 0 or NULL.

The default number of bins is 10.

exclude_list

List of numeric columns to be excluded from the binning process. If you do not specify exclude_list, all numeric columns in the data source are binned.

The format of exclude_list is:

dbms_data_mining_transform.COLUMN_LIST('col1','col2',
                                       ...'coln')

round_num

Specifies how to round the number in the VAL column of the transformation definition table.

When round_num is positive, it specifies the most significant digits to retain. When round_num is negative, it specifies the least significant digits to remove. In both cases, the result is rounded to the specified number of digits. See the Usage Notes for an example.

The default value of round_num is 6.

bin_schema_name

Schema of bin_table_name. If no schema is specified, the current schema is used.

data_schema_name

Schema of data_table_name. If no schema is specified, the current schema is used.


Usage Notes

  1. The round_num parameter controls the rounding of column values in the transformation definition table, as follows:

    For a value of 308.162:
    when round_num =  1       result is 300
    when round_num =  2       result is 310
    when round_num =  3       result is 308
    when round_num =  0       result is 308.162
    when round_num = -1       result is 308.16
    when round_num = -2       result is 308.2
    
  2. INSERT_BIN_NUM_EQWIDTH ignores columns with all NULL values or only one unique value.

Examples

In this example, INSERT_BIN_NUM_EQWIDTH computes the bin boundaries for the affinity_card column in mining_data_build and inserts the transformations in a transformation definition table. The STACK_BIN_NUM Procedure creates a transformation list from the contents of the definition table. The CREATE_MODEL Procedure embeds the transformation list in a new model called glm_model.

The transformation and reverse transformation expressions embedded in glm_model are returned by the GET_MODEL_TRANSFORMATIONS Function.

CREATE OR REPLACE VIEW mining_data AS
       SELECT cust_id, cust_income_level, cust_gender, affinity_card
       FROM mining_data_build;
 
DESCRIBE mining_data
 Name                      Null?    Type
 ------------------------- -------- -----------------
 CUST_ID                   NOT NULL NUMBER
 CUST_INCOME_LEVEL                  VARCHAR2(30)
 CUST_GENDER                        VARCHAR2(1)
 AFFINITY_CARD                      NUMBER(10)
 
BEGIN
    dbms_data_mining_transform.CREATE_BIN_NUM(
        bin_table_name   => 'bin_tbl');
    dbms_data_mining_transform.INSERT_BIN_NUM_EQWIDTH (
        bin_table_name   => 'bin_tbl',
        data_table_name  => 'mining_data',
        bin_num          => 4,
        exclude_list     => dbms_data_mining_transform.COLUMN_LIST('cust_id'));
END;
/
 
set numwidth 10
column val off
column col format a20
column bin format a10
SELECT col, val, bin FROM bin_tbl
    ORDER BY val ASC;
 
COL                         VAL  BIN
-------------------- ----------  ----------
AFFINITY_CARD                 0
AFFINITY_CARD               .25  1
AFFINITY_CARD                .5  2
AFFINITY_CARD               .75  3
AFFINITY_CARD                 1  4
 
CREATE TABLE glmsettings(
        setting_name  VARCHAR2(30),
        setting_value VARCHAR2(30));
 
BEGIN
   INSERT INTO glmsettings (setting_name, setting_value) VALUES
         (dbms_data_mining.algo_name, dbms_data_mining.algo_generalized_linear_model);
   COMMIT;
END;
/
 
DECLARE
     xforms   dbms_data_mining_transform.TRANSFORM_LIST;
BEGIN
     dbms_data_mining_transform.STACK_BIN_NUM (
         bin_table_name           => 'bin_tbl',
         xform_list               =>  xforms,
         literal_flag             =>  TRUE);
     dbms_data_mining.CREATE_MODEL(
         model_name              => 'glm_model',
         mining_function         => dbms_data_mining.regression,
         data_table_name         => 'mining_data',
         case_id_column_name     => 'cust_id',
         target_column_name      => 'affinity_card',
         settings_table_name     => 'glmsettings',
         data_schema_name        => null,
         settings_schema_name    => null,
         xform_list              => xforms);
END;
/
 
SELECT attribute_name 
      FROM TABLE(dbms_data_mining.GET_MODEL_TRANSFORMATIONS('glm_model'));
 
ATTRIBUTE_NAME
------------------------
AFFINITY_CARD
 
SELECT expression 
       FROM TABLE(dbms_data_mining.GET_MODEL_TRANSFORMATIONS('glm_model'));
 
EXPRESSION
--------------------------------------------------------------------------------
CASE WHEN "AFFINITY_CARD"<0 THEN NULL WHEN "AFFINITY_CARD"<=.25 THEN 1 WHEN
"AFFINITY_CARD"<=.5 THEN 2 WHEN "AFFINITY_CARD"<=.75 THEN 3 WHEN
"AFFINITY_CARD"<=1 THEN 4 END
 
SELECT reverse_expression 
       FROM TABLE(dbms_data_mining.GET_MODEL_TRANSFORMATIONS('glm_model'));
 
REVERSE_EXPRESSION
--------------------------------------------------------------------------------
DECODE("AFFINITY_CARD",4,'(.75; 1]',1,'[0; .25]',2,'(.25; .5]',3,'(.5; .75]',
NULL,'( ; 0), (1;  ), NULL')

INSERT_BIN_NUM_QTILE Procedure

This procedure performs numerical binning and inserts the transformation definitions in a transformation definition table. The procedure calls the SQL NTILE function to order the data and divide it equally into the specified number of bins (quantiles).

INSERT_BIN_NUM_QTILE bins all the NUMBER and FLOAT columns in the data source unless you specify a list of columns to ignore.

Syntax

DBMS_DATA_MINING_TRANSFORM.INSERT_BIN_NUM_QTILE (
    bin_table_name       IN VARCHAR2,
    data_table_name      IN VARCHAR2,
    bin_num              IN PLS_INTEGER DEFAULT 10,
    exclude_list         IN COLUMN_LIST DEFAULT NULL,
    bin_schema_name      IN VARCHAR2 DEFAULT NULL,
    data_schema_name     IN VARCHAR2 DEFAULT NULL);

Parameters

Table 45-23 INSERT_BIN_NUM_QTILE Procedure Parameters

Parameter Description

bin_table_name

Name of the transformation definition table for numerical binning. You can use the CREATE_BIN_NUM Procedure to create the definition table. The following columns are required:

COL       VARCHAR2(30)
VAL       NUMBER
BIN       VARCHAR2(4000)

CREATE_BIN_NUM creates an additional column, ATT, which may be used for specifying nested attributes. This column is not used by INSERT_BIN_NUM_QTILE.

data_table_name

Name of the table containing the data to be transformed

bin_num

Number of bins. No binning occurs if bin_num is 0 or NULL.

The default number of bins is 10.

exclude_list

List of numeric columns to be excluded from the binning process. If you do not specify exclude_list, all numeric columns in the data source are binned.

The format of exclude_list is:

dbms_data_mining_transform.COLUMN_LIST('col1','col2',
                                       ...'coln')

bin_schema_name

Schema of bin_table_name. If no schema is specified, the current schema is used.

data_schema_name

Schema of data_table_name. If no schema is specified, the current schema is used.


Usage Notes

  1. After dividing the data into quantiles, the NTILE function distributes any remainder values one for each quantile, starting with the first. See Oracle Database SQL Language Reference for details.

  2. Columns with all NULL values are ignored by INSERT_BIN_NUM_QTILE.

Examples

In this example, INSERT_BIN_NUM_QTILE computes the bin boundaries for the cust_year_of_birth and cust_credit_limit columns in sh.customers and inserts the transformations in a transformation definition table. The STACK_BIN_NUM Procedure creates a transformation list from the contents of the definition table.

The SQL expression that computes the transformation is shown in STACK_VIEW. The view is for display purposes only; it cannot be used to embed the transformations in a model.

CREATE OR REPLACE VIEW mining_data AS
        SELECT cust_id, cust_year_of_birth, cust_credit_limit, cust_city
        FROM sh.customers;
 
DESCRIBE mining_data
 Name                                    Null?    Type
 --------------------------------------- -------- -----------------------------
 CUST_ID                                 NOT NULL NUMBER
 CUST_YEAR_OF_BIRTH                      NOT NULL NUMBER(4)
 CUST_CREDIT_LIMIT                       NUMBER
 CUST_CITY                               NOT NULL VARCHAR2(30)
 
BEGIN
   dbms_data_mining_transform.CREATE_BIN_NUM(
        bin_table_name   => 'bin_tbl');
   dbms_data_mining_transform.INSERT_BIN_NUM_QTILE (
        bin_table_name   => 'bin_tbl',
        data_table_name  => 'mining_data',
        bin_num          => 3,
        exclude_list     => dbms_data_mining_transform.COLUMN_LIST('cust_id'));
END;
/
 
set numwidth 8
column val off
column col format a20
column bin format a10
SELECT col, val, bin
      FROM bin_tbl
      ORDER BY col ASC, val ASC;
 
COL                       VAL BIN
-------------------- -------- ----------
CUST_CREDIT_LIMIT        1500
CUST_CREDIT_LIMIT        3000 1
CUST_CREDIT_LIMIT        9000 2
CUST_CREDIT_LIMIT       15000 3
CUST_YEAR_OF_BIRTH       1913
CUST_YEAR_OF_BIRTH       1949 1
CUST_YEAR_OF_BIRTH       1965 2
CUST_YEAR_OF_BIRTH       1990 3
 
DECLARE
   xforms   dbms_data_mining_transform.TRANSFORM_LIST;
BEGIN
   dbms_data_mining_transform.STACK_BIN_NUM (
        bin_table_name        => 'bin_tbl',
        xform_list            =>  xforms);
   dbms_data_mining_transform.XFORM_STACK (
        xform_list            =>  xforms,
        data_table_name       => 'mining_data',
        xform_view_name       => 'stack_view');
END;
/
 
set long 3000
SELECT text FROM user_views WHERE view_name in 'STACK_VIEW';
 
TEXT
--------------------------------------------------------------------------------
SELECT "CUST_ID",CASE WHEN "CUST_YEAR_OF_BIRTH"<1913 THEN NULL WHEN "CUST_YEAR_O
F_BIRTH"<=1949 THEN '1' WHEN "CUST_YEAR_OF_BIRTH"<=1965 THEN '2' WHEN "CUST_YEAR
_OF_BIRTH"<=1990 THEN '3' END "CUST_YEAR_OF_BIRTH",CASE WHEN "CUST_CREDIT_LIMIT"
<1500 THEN NULL WHEN "CUST_CREDIT_LIMIT"<=3000 THEN '1' WHEN "CUST_CREDIT_LIMIT"
<=9000 THEN '2' WHEN "CUST_CREDIT_LIMIT"<=15000 THEN '3' END "CUST_CREDIT_LIMIT"
,"CUST_CITY" FROM mining_data

INSERT_BIN_SUPER Procedure

This procedure performs numerical and categorical binning and inserts the transformation definitions in transformation definition tables. The procedure computes bin boundaries based on intrinsic relationships between predictors and a target.

INSERT_BIN_SUPER uses an intelligent binning technique known as supervised binning. It builds a single-predictor decision tree and derives the bin boundaries from splits within the tree.

INSERT_BIN_SUPER bins all the VARCHAR2, CHAR, NUMBER, and FLOAT columns in the data source unless you specify a list of columns to ignore.

Syntax

DBMS_DATA_MINING_TRANSFORM.INSERT_BIN_SUPER (
    num_table_name        IN VARCHAR2,
    cat_table_name        IN VARCHAR2,
    data_table_name       IN VARCHAR2,
    target_column_name    IN VARCHAR2,
    max_bin_num           IN PLS_INTEGER  DEFAULT 1000,
    exclude_list          IN COLUMN_LIST  DEFAULT NULL,
    num_schema_name       IN VARCHAR2     DEFAULT NULL,
    cat_schema_name       IN VARCHAR2     DEFAULT NULL,
    data_schema_name      IN VARCHAR2     DEFAULT NULL,
    rem_table_name        IN VARCHAR2     DEFAULT NULL,
    rem_schema_name       IN VARCHAR2     DEFAULT NULL);

Parameters

Table 45-24 INSERT_BIN_SUPER Procedure Parameters

Parameter Description

num_table_name

Name of the transformation definition table for numerical binning. You can use the CREATE_BIN_NUM Procedure to create the definition table. The following columns are required:

COL       VARCHAR2(30)
VAL       VNUMBER
BIN       VARCHAR2(4000)

CREATE_BIN_NUM creates an additional column, ATT, which may be used for specifying nested attributes. This column is not used by INSERT_BIN_SUPER.

cat_table_name

Name of the transformation definition table for categorical binning. You can use the CREATE_BIN_CAT Procedure to create the definition table. The following columns are required:

COL       VARCHAR2(30)
VAL       VARCHAR2(4000)
BIN       VARCHAR2(4000)

CREATE_BIN_CAT creates an additional column, ATT, which is used for specifying nested attributes. This column is not used by INSERT_BIN_SUPER.

data_table_name

Name of the table containing the data to be transformed

target_column_name

Name of a column to be used as the target for the decision tree models

max_bin_num

The maximum number of bins. The default is 1000.

exclude_list

List of columns to be excluded from the binning process. If you do not specify exclude_list, all CHAR, VARCHAR2, NUMBER, and FLOAT columns in the data source are binned.

The format of exclude_list is:

dbms_data_mining_transform.COLUMN_LIST('col1','col2',
                                       ...'coln')

num_schema_name

Schema of num_table_name. If no schema is specified, the current schema is used.

cat_schema_name

Schema of cat_table_name. If no schema is specified, the current schema is used.

data_schema_name

Schema of data_table_name. If no schema is specified, the current schema is used.

rem_table_name

Name of a column removal definition table. The table must have the columns described in "CREATE_COL_REM Procedure". You can use CREATE_COL_REM to create the table. See Usage Notes.

rem_schema_name

Schema of rem_table_name. If no schema is specified, the current schema is used.


Usage Notes

  1. Columns that have no significant splits are not binned. You can remove the unbinned columns from the mining data by specifying a column removal definition table. If you do not specify a column removal definition table, the unbinned columns remain in the mining data.

  2. See Oracle Data Mining Concepts to learn more about decision trees in Oracle Data Mining

Examples

In this example, INSERT_BIN_SUPER computes the bin boundaries for predictors of cust_credit_limit and inserts the transformations in transformation definition tables. One predictor is numerical, the other is categorical. (INSERT_BIN_SUPER determines that the cust_postal_code column is not a significant predictor.) STACK procedures create transformation lists from the contents of the definition tables.

The SQL expressions that compute the transformations are shown in the views MINING_DATA_STACK_NUM and MINING_DATA_STACK_CAT. The views are for display purposes only; they cannot be used to embed the transformations in a model.

CREATE OR REPLACE VIEW mining_data AS
     SELECT cust_id, cust_year_of_birth, cust_marital_status,
            cust_postal_code, cust_credit_limit
     FROM sh.customers;
 
DESCRIBE mining_data
 Name                             Null?    Type
 -------------------------------- -------- ------------------------------------
 CUST_ID                          NOT NULL NUMBER
 CUST_YEAR_OF_BIRTH               NOT NULL NUMBER(4)
 CUST_MARITAL_STATUS                       VARCHAR2(20)
 CUST_POSTAL_CODE                 NOT NULL VARCHAR2(10)
 CUST_CREDIT_LIMIT                         NUMBER
 
BEGIN
    dbms_data_mining_transform.CREATE_BIN_NUM(
         bin_table_name      => 'bin_num_tbl');
    dbms_data_mining_transform.CREATE_BIN_CAT(
         bin_table_name      => 'bin_cat_tbl');
    dbms_data_mining_transform.CREATE_COL_REM(
         rem_table_name      => 'rem_tbl');
END;
/
 
BEGIN
   COMMIT;
   dbms_data_mining_transform.INSERT_BIN_SUPER (
      num_table_name      => 'bin_num_tbl',
      cat_table_name      => 'bin_cat_tbl',
      data_table_name     => 'mining_data',
      target_column_name  => 'cust_credit_limit',
      max_bin_num         =>  4,
      exclude_list        =>  dbms_data_mining_transform.COLUMN_LIST('cust_id'),
      num_schema_name     => 'dmuser',
      cat_schema_name     => 'dmuser',
      data_schema_name    => 'dmuser',
      rem_table_name      => 'rem_tbl',
      rem_schema_name     => 'dmuser');
   COMMIT;
END;
/
 
set numwidth 8
column val off
SELECT col, val, bin FROM bin_num_tbl
      ORDER BY bin ASC;
 
COL                       VAL BIN
-------------------- -------- ----------
CUST_YEAR_OF_BIRTH     1923.5 1
CUST_YEAR_OF_BIRTH     1923.5 1
CUST_YEAR_OF_BIRTH     1945.5 2
CUST_YEAR_OF_BIRTH     1980.5 3
CUST_YEAR_OF_BIRTH            4
 
column val on
column val format a20
SELECT col, val, bin FROM bin_cat_tbl
      ORDER BY bin ASC;
 
COL                  VAL                  BIN
-------------------- -------------------- ----------
CUST_MARITAL_STATUS  married              1
CUST_MARITAL_STATUS  single               2
CUST_MARITAL_STATUS  Mar-AF               3
CUST_MARITAL_STATUS  Mabsent              3
CUST_MARITAL_STATUS  Divorc.              3
CUST_MARITAL_STATUS  Married              3
CUST_MARITAL_STATUS  Widowed              3
CUST_MARITAL_STATUS  NeverM               3
CUST_MARITAL_STATUS  Separ.               3
CUST_MARITAL_STATUS  divorced             4
CUST_MARITAL_STATUS  widow                4
 
SELECT col from rem_tbl;
 
COL
--------------------
CUST_POSTAL_CODE
 
DECLARE
    xforms_num      dbms_data_mining_transform.TRANSFORM_LIST;
    xforms_cat      dbms_data_mining_transform.TRANSFORM_LIST;
    BEGIN
       dbms_data_mining_transform.STACK_BIN_NUM (
            bin_table_name    => 'bin_num_tbl',
            xform_list        => xforms_num);
       dbms_data_mining_transform.XFORM_STACK (
            xform_list         => xforms_num,
            data_table_name    => 'mining_data',
            xform_view_name    => 'mining_data_stack_num');
       dbms_data_mining_transform.STACK_BIN_CAT (
             bin_table_name    => 'bin_cat_tbl',
             xform_list        => xforms_cat);
       dbms_data_mining_transform.XFORM_STACK (
             xform_list         => xforms_cat,
             data_table_name    => 'mining_data',
             xform_view_name    => 'mining_data_stack_cat');
   END;
 /
 
set long 3000
SELECT text FROM user_views WHERE view_name IN 'MINING_DATA_STACK_NUM';
 
TEXT
--------------------------------------------------------------------------------
SELECT "CUST_ID",CASE WHEN "CUST_YEAR_OF_BIRTH"<1923.5 THEN '1' WHEN "CUST_YEAR_
OF_BIRTH"<=1923.5 THEN '1' WHEN "CUST_YEAR_OF_BIRTH"<=1945.5 THEN '2' WHEN "CUST
_YEAR_OF_BIRTH"<=1980.5 THEN '3' WHEN "CUST_YEAR_OF_BIRTH" IS NOT NULL THEN '4'
END "CUST_YEAR_OF_BIRTH","CUST_MARITAL_STATUS","CUST_POSTAL_CODE","CUST_CREDIT_L
IMIT" FROM mining_data
 
 
SELECT text FROM user_views WHERE view_name IN 'MINING_DATA_STACK_CAT';
 
TEXT
--------------------------------------------------------------------------------
SELECT "CUST_ID","CUST_YEAR_OF_BIRTH",DECODE("CUST_MARITAL_STATUS",'Divorc.','3'
,'Mabsent','3','Mar-AF','3','Married','3','NeverM','3','Separ.','3','Widowed','3
','divorced','4','married','1','single','2','widow','4') "CUST_MARITAL_STATUS","
CUST_POSTAL_CODE","CUST_CREDIT_LIMIT" FROM mining_data

INSERT_CLIP_TRIM_TAIL Procedure

This procedure replaces numeric outliers with nulls and inserts the transformation definitions in a transformation definition table.

INSERT_CLIP_TRIM_TAIL computes the boundaries of the data based on a specified percentage. It removes the values that fall outside the boundaries (tail values) from the data. If you wish to replace the tail values instead of removing them, use the INSERT_CLIP_WINSOR_TAIL Procedure.

INSERT_CLIP_TRIM_TAIL clips all the NUMBER and FLOAT columns in the data source unless you specify a list of columns to ignore.

Syntax

DBMS_DATA_MINING_TRANSFORM.INSERT_CLIP_TRIM_TAIL (
    clip_table_name     IN VARCHAR2,
    data_table_name     IN VARCHAR2,
    tail_frac           IN NUMBER DEFAULT 0.025,
    exclude_list        IN COLUMN_LIST DEFAULT NULL,
    clip_schema_name    IN VARCHAR2 DEFAULT NULL,
    data_schema_name    IN VARCHAR2 DEFAULT NULL);

Parameters

Table 45-25 INSERT_CLIP_TRIM_TAIL Procedure Parameters

Parameter Description

clip_table_name

Name of the transformation definition table for numerical clipping. You can use the CREATE_CLIP Procedure to create the definition table. The following columns are required:

COL         VARCHAR2(30)
 LCUT        NUMBER
 LVAL        NUMBER
 RCUT        NUMBER
 RVAL        NUMBER

CREATE_CLIP creates an additional column, ATT, which may be used for specifying nested attributes. This column is not used by INSERT_CLIP_TRIM_TAIL.

data_table_name

Name of the table containing the data to be transformed

tail_frac

The percentage of non-null values to be designated as outliers at each end of the data. For example, if tail_frac is .01, then 1% of the data at the low end and 1% of the data at the high end will be treated as outliers.

If tail_frac is greater than or equal to .5, no clipping occurs.

The default value of tail_frac is 0.025.

exclude_list

List of NUMBER columns to be excluded from the clipping process. If you do not specify exclude_list, all NUMBER columns in the data are clipped.

The format of exclude_list is:

dbms_data_mining_transform.COLUMN_LIST('col1','col2',
                                       ...'coln')

clip_schema_name

Schema of clip_table_name. If no schema is specified, the current schema is used.

data_schema_name

Schema of data_table_name. If no schema is specified, the current schema is used.


Usage Notes

The DBMS_DATA_MINING_TRANSFORM package provides two clipping procedures: INSERT_CLIP_TRIM_TAIL and INSERT_CLIP_WINSOR_TAIL. Both procedures compute the boundaries as follows:

INSERT_CLIP_TRIM_TAIL replaces the outliers with nulls, effectively removing them from the data.

INSERT_CLIP_WINSOR_TAIL assigns lcut to the low outliers and rcut to the high outliers.

Examples

In this example, INSERT_CLIP_TRIM_TAIL trims 10% of the data in two columns (5% from the high end and 5% from the low end) and inserts the transformations in a transformation definition table. The STACK_CLIP Procedure creates a transformation list from the contents of the definition table.

The SQL expression that computes the trimming is shown in the view MINING_DATA_STACK. The view is for display purposes only; it cannot be used to embed the transformations in a model.

CREATE OR REPLACE VIEW mining_data AS
       SELECT cust_id, cust_year_of_birth, cust_credit_limit, cust_city
       FROM sh.customers;
 
DESCRIBE mining_data
 Name                            Null?    Type
 ------------------------------- -------- -------------------
 CUST_ID                         NOT NULL NUMBER
 CUST_YEAR_OF_BIRTH              NOT NULL NUMBER(4)
 CUST_CREDIT_LIMIT                        NUMBER
 CUST_CITY                       NOT NULL VARCHAR2(30)

BEGIN
   dbms_data_mining_transform.CREATE_CLIP(
      clip_table_name    => 'clip_tbl');
   dbms_data_mining_transform.INSERT_CLIP_TRIM_TAIL(
      clip_table_name    => 'clip_tbl',
      data_table_name    => 'mining_data',
      tail_frac          => 0.05,
      exclude_list       => DBMS_DATA_MINING_TRANSFORM.COLUMN_LIST('cust_id'));
END;
/
 
SELECT col, lcut, lval, rcut, rval
      FROM clip_tbl
      ORDER BY col ASC;
 
COL                      LCUT     LVAL     RCUT     RVAL
-------------------- -------- -------- -------- --------
CUST_CREDIT_LIMIT        1500             11000
CUST_YEAR_OF_BIRTH       1934              1982
 
DECLARE
     xforms      dbms_data_mining_transform.TRANSFORM_LIST;
BEGIN
     dbms_data_mining_transform.STACK_CLIP (
          clip_table_name    => 'clip_tbl',
          xform_list         => xforms);
     dbms_data_mining_transform.XFORM_STACK (
          xform_list         => xforms,
          data_table_name    => 'mining_data',
          xform_view_name    => 'mining_data_stack');
 END;
 /
 
set long 3000
SELECT text FROM user_views WHERE view_name IN 'MINING_DATA_STACK';
 
TEXT
--------------------------------------------------------------------------------
SELECT "CUST_ID",CASE WHEN "CUST_YEAR_OF_BIRTH" < 1934 THEN NULL WHEN "CUST_YEAR
_OF_BIRTH" > 1982 THEN NULL ELSE "CUST_YEAR_OF_BIRTH" END "CUST_YEAR_OF_BIRTH",C
ASE WHEN "CUST_CREDIT_LIMIT" < 1500 THEN NULL WHEN "CUST_CREDIT_LIMIT" > 11000 T
HEN NULL ELSE "CUST_CREDIT_LIMIT" END "CUST_CREDIT_LIMIT","CUST_CITY" FROM minin
g_data

INSERT_CLIP_WINSOR_TAIL Procedure

This procedure replaces numeric outliers with the upper or lower boundary values. It inserts the transformation definitions in a transformation definition table.

INSERT_CLIP_WINSOR_TAIL computes the boundaries of the data based on a specified percentage. It replaces the values that fall outside the boundaries (tail values) with the related boundary value. If you wish to set tail values to null, use the INSERT_CLIP_TRIM_TAIL Procedure.

INSERT_CLIP_WINSOR_TAIL clips all the NUMBER and FLOAT columns in the data source unless you specify a list of columns to ignore.

Syntax

DBMS_DATA_MINING_TRANSFORM.INSERT_CLIP_WINSOR_TAIL (
    clip_table_name    IN VARCHAR2,
    data_table_name    IN VARCHAR2,
    tail_frac          IN NUMBER DEFAULT 0.025,
    exclude_list       IN COLUMN_LIST DEFAULT NULL,
    clip_schema_name   IN VARCHAR2 DEFAULT NULL,
    data_schema_name   IN VARCHAR2 DEFAULT NULL);

Parameters

Table 45-26 INSERT_CLIP_WINSOR_TAIL Procedure Parameters

Parameter Description

clip_table_name

Name of the transformation definition table for numerical clipping. You can use the CREATE_CLIP Procedure to create the definition table. The following columns are required:

COL         VARCHAR2(30)
 LCUT        NUMBER
 LVAL        NUMBER
 RCUT        NUMBER
 RVAL        NUMBER

CREATE_CLIP creates an additional column, ATT, which may be used for specifying nested attributes. This column is not used by INSERT_CLIP_WINSOR_TAIL.

data_table_name

Name of the table containing the data to be transformed

tail_frac

The percentage of non-null values to be designated as outliers at each end of the data. For example, if tail_frac is .01, then 1% of the data at the low end and 1% of the data at the high end will be treated as outliers.

If tail_frac is greater than or equal to .5, no clipping occurs.

The default value of tail_frac is 0.025.

exclude_list

List of NUMBER columns to be excluded from the clipping process. If you do not specify exclude_list, all NUMBER columns in the data are clipped.

The format of exclude_list is:

dbms_data_mining_transform.COLUMN_LIST('col1','col2',
                                       ...'coln')

clip_schema_name

Schema of clip_table_name. If no schema is specified, the current schema is used.

data_schema_name

Schema of data_table_name. If no schema is specified, the current schema is used.


Usage Notes

The DBMS_DATA_MINING_TRANSFORM package provides two clipping procedures: INSERT_CLIP_WINSOR_TAIL and INSERT_CLIP_TRIM_TAIL. Both procedures compute the boundaries as follows:

INSERT_CLIP_WINSOR_TAIL assigns lcut to the low outliers and rcut to the high outliers.

INSERT_CLIP_TRIM_TAIL replaces the outliers with nulls, effectively removing them from the data.

Examples

In this example, INSERT_CLIP_WINSOR_TAIL winsorizes 10% of the data in two columns (5% from the high end, and 5% from the low end) and inserts the transformations in a transformation definition table. The STACK_CLIP Procedure creates a transformation list from the contents of the definition table.

The SQL expression that computes the transformation is shown in the view MINING_DATA_STACK. The view is for display purposes only; it cannot be used to embed the transformations in a model.

CREATE OR REPLACE VIEW mining_data AS
        SELECT cust_id, cust_year_of_birth, cust_credit_limit, cust_city
        FROM sh.customers;

describe mining_data
 Name                                     Null?    Type
 ---------------------------------------- -------- -------------
 CUST_ID                                  NOT NULL NUMBER
 CUST_YEAR_OF_BIRTH                       NOT NULL NUMBER(4)
 CUST_CREDIT_LIMIT                                 NUMBER
 CUST_CITY                                NOT NULL VARCHAR2(30)
 
BEGIN
  dbms_data_mining_transform.CREATE_CLIP(
      clip_table_name   => 'clip_tbl');
  dbms_data_mining_transform.INSERT_CLIP_WINSOR_TAIL(
      clip_table_name   => 'clip_tbl',
      data_table_name   => 'mining_data',
      tail_frac         => 0.05,
      exclude_list      => DBMS_DATA_MINING_TRANSFORM.COLUMN_LIST('cust_id'));
END;
/

SELECT col, lcut, lval, rcut, rval FROM clip_tbl
   ORDER BY col ASC;
COL                                LCUT     LVAL     RCUT     RVAL
------------------------------ -------- -------- -------- --------
CUST_CREDIT_LIMIT                  1500     1500    11000    11000
CUST_YEAR_OF_BIRTH                 1934     1934     1982     1982

DECLARE
   xforms      dbms_data_mining_transform.TRANSFORM_LIST;
BEGIN
   dbms_data_mining_transform.STACK_CLIP (
   clip_table_name    => 'clip_tbl',
   xform_list        => xforms);
dbms_data_mining_transform.XFORM_STACK (
   xform_list         => xforms,
   data_table_name    => 'mining_data',
   xform_view_name    => 'mining_data_stack');
END;
/
 
set long 3000
SQL> SELECT text FROM user_views WHERE view_name IN 'MINING_DATA_STACK';
 
TEXT
--------------------------------------------------------------------------------
SELECT "CUST_ID",CASE WHEN "CUST_YEAR_OF_BIRTH" < 1934 THEN 1934 WHEN "CUST_YEAR
_OF_BIRTH" > 1982 THEN 1982 ELSE "CUST_YEAR_OF_BIRTH" END "CUST_YEAR_OF_BIRTH",C
ASE WHEN "CUST_CREDIT_LIMIT" < 1500 THEN 1500 WHEN "CUST_CREDIT_LIMIT" > 11000 T
HEN 11000 ELSE "CUST_CREDIT_LIMIT" END "CUST_CREDIT_LIMIT","CUST_CITY" FROM mini
ng_data

INSERT_MISS_CAT_MODE Procedure

This procedure replaces missing categorical values with the value that occurs most frequently in the column (the mode). It inserts the transformation definitions in a transformation definition table.

INSERT_MISS_CAT_MODE replaces missing values in all VARCHAR2 and CHAR columns in the data source unless you specify a list of columns to ignore.

Syntax

DBMS_DATA_MINING_TRANSFORM.INSERT_MISS_CAT_MODE (
    miss_table_name    IN VARCHAR2,
    data_table_name    IN VARCHAR2,
    exclude_list       IN COLUMN_LIST DEFAULT NULL,
    miss_schema_name   IN VARCHAR2 DEFAULT NULL,
    data_schema_name   IN VARCHAR2 DEFAULT NULL);

Parameters

Table 45-27 INSERT_MISS_CAT_MODE Procedure Parameters

Parameter Description

miss_table_name

Name of the transformation definition table for categorical missing value treatment. You can use the CREATE_MISS_CAT Procedure to create the definition table. The following columns are required:

COL         VARCHAR2(30)
 VAL         VARCHAR2(4000)

CREATE_MISS_CAT creates an additional column, ATT, which may be used for specifying nested attributes. This column is not used by INSERT_MISS_CAT_MODE.

data_table_name

Name of the table containing the data to be transformed

exclude_list

List of VARCHAR2 and CHAR columns to be excluded from missing value treatment. If you do not specify exclude_list, all VARCHAR2 and CHAR columns are transformed.

The format of exclude_list is:

dbms_data_mining_transform.COLUMN_LIST('col1','col2',
                                       ...'coln')

miss_schema_name

Schema of miss_table_name. If no schema is specified, the current schema is used.

data_schema_name

Schema of data_table_name. If no schema is specified, the current schema is used.


Usage Notes

If you wish to replace categorical missing values with a value other than the mode, you can edit the transformation definition table.

See Also:

Oracle Data Mining Application Developer's Guide for information about default missing value treatment in Oracle Data Mining

Example

In this example, INSERT_MISS_CAT_MODE computes missing value treatment for cust_city and inserts the transformation in a transformation definition table. The STACK_MISS_CAT Procedure creates a transformation list from the contents of the definition table.

The SQL expression that computes the transformation is shown in the view MINING_DATA_STACK. The view is for display purposes only; it cannot be used to embed the transformations in a model.

CREATE OR REPLACE VIEW mining_data AS
         SELECT cust_id, cust_year_of_birth, cust_city
         FROM sh.customers;
 
describe mining_data
 Name                             Null?    Type
 -------------------------------- -------- ----------------
 CUST_ID                          NOT NULL NUMBER
 CUST_YEAR_OF_BIRTH               NOT NULL NUMBER(4)
 CUST_CITY                        NOT NULL VARCHAR2(30)
 
BEGIN
  dbms_data_mining_transform.create_miss_cat(
      miss_table_name   => 'missc_tbl');
  dbms_data_mining_transform.insert_miss_cat_mode(
      miss_table_name   => 'missc_tbl',
      data_table_name   => 'mining_data');
END;
/

SELECT stats_mode(cust_city) FROM mining_data;
 
STATS_MODE(CUST_CITY)
------------------------------
Los Angeles

SELECT col, val
    from missc_tbl;

COL                            VAL
------------------------------ ------------------------------
CUST_CITY                      Los Angeles

DECLARE
    xforms      dbms_data_mining_transform.TRANSFORM_LIST;
BEGIN
    dbms_data_mining_transform.STACK_MISS_CAT (
        miss_table_name    => 'missc_tbl',
        xform_list        => xforms);
    dbms_data_mining_transform.XFORM_STACK (
         xform_list         => xforms,
         data_table_name    => 'mining_data',
         xform_view_name    => 'mining_data_stack');
END;
/
 
set long 3000
SELECT text FROM user_views WHERE view_name IN 'MINING_DATA_STACK';
 
TEXT
--------------------------------------------------------------------------------
SELECT "CUST_ID","CUST_YEAR_OF_BIRTH",NVL("CUST_CITY",'Los Angeles') "CUST_CITY"
 FROM mining_data

INSERT_MISS_NUM_MEAN Procedure

This procedure replaces missing numerical values with the average (the mean) and inserts the transformation definitions in a transformation definition table.

INSERT_MISS_NUM_MEAN replaces missing values in all NUMBER and FLOAT columns in the data source unless you specify a list of columns to ignore.

Syntax

DBMS_DATA_MINING_TRANSFORM.INSERT_MISS_NUM_MEAN (
    miss_table_name    IN VARCHAR2,
    data_table_name    IN VARCHAR2,
    exclude_list       IN COLUMN_LIST DEFAULT NULL,
    round_num          IN PLS_INTEGER DEFAULT 6,
    miss_schema_name   IN VARCHAR2 DEFAULT NULL,
    data_schema_name   IN VARCHAR2 DEFAULT NULL);

Parameters

Table 45-28 INSERT_MISS_NUM_MEAN Procedure Parameters

Parameter Description

miss_table_name

Name of the transformation definition table for numerical missing value treatment. You can use the CREATE_MISS_NUM Procedure to create the definition table.

The following columns are required by INSERT_MISS_NUM_MEAN:

COL          VARCHAR2(30)
 VAL          NUMBER

CREATE_MISS_NUM creates an additional column, ATT, which may be used for specifying nested attributes. This column is not used by INSERT_MISS_NUM_MEAN.

data_table_name

Name of the table containing the data to be transformed

exclude_list

List of NUMBER columns to be excluded from missing value treatment. If you do not specify exclude_list, all NUMBER columns are transformed.

The format of exclude_list is:

dbms_data_mining_transform.COLUMN_LIST('col1','col2',
                                       ...'coln')

round_num

The number of significant digits to use for the mean.

The default number is 6.

miss_schema_name

Schema of miss_table_name. If no schema is specified, the current schema is used.

data_schema_name

Schema of data_table_name. If no schema is specified, the current schema is used.


Usage Notes

If you wish to replace numerical missing values with a value other than the mean, you can edit the transformation definition table.

See Also:

Oracle Data Mining Application Developer's Guide for information about default missing value treatment in Oracle Data Mining

Example

In this example, INSERT_MISS_NUM_MEAN computes missing value treatment for cust_year_of_birth and inserts the transformation in a transformation definition table. The STACK_MISS_NUM Procedure creates a transformation list from the contents of the definition table.

The SQL expression that computes the transformation is shown in the view MINING_DATA_STACK. The view is for display purposes only; it cannot be used to embed the transformations in a model.

CREATE OR REPLACE VIEW mining_data AS
    SELECT cust_id, cust_year_of_birth, cust_city
    FROM sh.customers;

DESCRIBE mining_data
 Name                                       Null?    Type
 ------------------------------------------ -------- -------------------
 CUST_ID                                    NOT NULL NUMBER
 CUST_YEAR_OF_BIRTH                         NOT NULL NUMBER(4)
 CUST_CITY                                  NOT NULL VARCHAR2(30)
 
BEGIN
   dbms_data_mining_transform.create_miss_num(
       miss_table_name   => 'missn_tbl');
   dbms_data_mining_transform.insert_miss_num_mean(
       miss_table_name   => 'missn_tbl',
       data_table_name   => 'mining_data',
       exclude_list      => DBMS_DATA_MINING_TRANSFORM.COLUMN_LIST('cust_id'));
END;
/
 
set numwidth 4
column val off
SELECT col, val
  FROM missn_tbl;
 
COL                   VAL
-------------------- ----
CUST_YEAR_OF_BIRTH   1957
 
SELECT avg(cust_year_of_birth) FROM mining_data;
 
AVG(CUST_YEAR_OF_BIRTH)
-----------------------
                   1957
 
DECLARE
    xforms      dbms_data_mining_transform.TRANSFORM_LIST;
BEGIN
    dbms_data_mining_transform.STACK_MISS_NUM (
         miss_table_name    => 'missn_tbl',
         xform_list        => xforms);
    dbms_data_mining_transform.XFORM_STACK (
         xform_list         => xforms,
         data_table_name    => 'mining_data',
         xform_view_name    => 'mining_data_stack');
END;
/
 
set long 3000
SELECT text FROM user_views WHERE view_name IN 'MINING_DATA_STACK';
 
TEXT
--------------------------------------------------------------------------------
SELECT "CUST_ID",NVL("CUST_YEAR_OF_BIRTH",1957.4) "CUST_YEAR_OF_BIRTH","CUST_CIT
Y" FROM mining_data

INSERT_NORM_LIN_MINMAX Procedure

This procedure performs linear normalization and inserts the transformation definitions in a transformation definition table. INSERT_NORM_LIN_MINMAX computes the minimum and maximum values from the data and sets the value of shift and scale as follows:

shift = min
scale = max - min

Normalization is computed as:

x_new = (x_old - shift)/scale

INSERT_NORM_LIN_MINMAX rounds the value of scale to a specified number of significant digits before storing it in the transformation definition table.

INSERT_NORM_LIN_MINMAX normalizes all the NUMBER and FLOAT columns in the data source unless you specify a list of columns to ignore.

Syntax

DBMS_DATA_MINING_TRANSFORM.INSERT_NORM_LIN_MINMAX (
     norm_table_name     IN VARCHAR2,
     data_table_name     IN VARCHAR2,
     exclude_list        IN COLUMN_LIST DEFAULT NULL,
     round_num           IN PLS_INTEGER DEFAULT 6,
     norm_schema_name    IN VARCHAR2 DEFAULT NULL,
     data_schema_name    IN VARCHAR2 DEFAULT NULL);

Parameters

Table 45-29 INSERT_NORM_LIN_MINMAX Procedure Parameters

Parameter Description

norm_table_name

Name of the transformation definition table for linear normalization. You can use the CREATE_NORM_LIN Procedure to create the definition table. The following columns are required:

COL          VARCHAR2(30)
 SHIFT        NUMBER
 SCALE        NUMBER

CREATE_NORM_LIN creates an additional column, ATT, which may be used for specifying nested attributes. This column is not used by INSERT_NORM_LIN_MINMAX.

data_table_name

Name of the table containing the data to be transformed

exclude_list

List of NUMBER columns to be excluded from normalization. If you do not specify exclude_list, all NUMBER columns are transformed.

The format of exclude_list is:

dbms_data_mining_transform.COLUMN_LIST('col1','col2',
                                       ...'coln')

round_num

The number of significant digits to use for the minimum and maximum. The default number is 6.

norm_schema_name

Schema of norm_table_name. If no schema is specified, the current schema is used.

data_schema_name

Schema of data_table_name. If no schema is specified, the current schema is used.


Examples

In this example, INSERT_NORM_LIN_MINMAX normalizes the cust_year_of_birth column and inserts the transformation in a transformation definition table. The STACK_NORM_LIN Procedure creates a transformation list from the contents of the definition table.

The SQL expression that computes the transformation is shown in the view MINING_DATA_STACK. The view is for display purposes only; it cannot be used to embed the transformations in a model.

CREATE OR REPLACE VIEW mining_data AS
         SELECT cust_id, cust_gender, cust_year_of_birth
         FROM sh.customers;

describe mining_data
 Name                                 Null?    Type
 ------------------------------------ -------- ----------------
 CUST_ID                              NOT NULL NUMBER
 CUST_GENDER                          NOT NULL CHAR(1)
 CUST_YEAR_OF_BIRTH                   NOT NULL NUMBER(4)
 
BEGIN
       dbms_data_mining_transform.CREATE_NORM_LIN(
         norm_table_name  => 'norm_tbl');
       dbms_data_mining_transform.INSERT_NORM_LIN_MINMAX(
         norm_table_name  => 'norm_tbl',
         data_table_name  => 'mining_data',
         exclude_list     => dbms_data_mining_transform.COLUMN_LIST( 'cust_id'),
         round_num        => 3);
END;
/
 
SELECT col, shift, scale FROM norm_tbl;
 
COL                                 SHIFT      SCALE
------------------------------ ---------- ----------
CUST_YEAR_OF_BIRTH                   1910         77

DECLARE
     xforms      dbms_data_mining_transform.TRANSFORM_LIST;
BEGIN
     dbms_data_mining_transform.STACK_NORM_LIN (
          norm_table_name    => 'norm_tbl',
          xform_list        => xforms);
     dbms_data_mining_transform.XFORM_STACK (
          xform_list         => xforms,
          data_table_name    => 'mining_data',
          xform_view_name    => 'mining_data_stack');
END;
/
 
set long 3000
SELECT text FROM user_views WHERE view_name IN 'MINING_DATA_STACK';
 
TEXT
--------------------------------------------------------------------------------
SELECT "CUST_ID","CUST_GENDER",("CUST_YEAR_OF_BIRTH"-1910)/77 "CUST_YEAR_OF_BIRT
H" FROM mining_data

INSERT_NORM_LIN_SCALE Procedure

This procedure performs linear normalization and inserts the transformation definitions in a transformation definition table. INSERT_NORM_LIN_SCALE computes the minimum and maximum values from the data and sets the value of shift and scale as follows:

shift = 0
scale = max(abs(max), abs(min))

Normalization is computed as:

x_new = (x_old)/scale

INSERT_NORM_LIN_SCALE rounds the value of scale to a specified number of significant digits before storing it in the transformation definition table.

INSERT_NORM_LIN_SCALE normalizes all the NUMBER and FLOAT columns in the data source unless you specify a list of columns to ignore.

Syntax

DBMS_DATA_MINING_TRANSFORM.INSERT_NORM_LIN_SCALE (
     norm_table_name     IN VARCHAR2,
     data_table_name     IN VARCHAR2,
     exclude_list        IN COLUMN_LIST DEFAULT NULL,
     round_num           IN PLS_INTEGER DEFAULT 6,
     norm_schema_name    IN VARCHAR2 DEFAULT NULL,
     data_schema_name    IN VARCHAR2 DEFAULT NULL);

Parameters

Table 45-30 INSERT_NORM_LIN_SCALE Procedure Parameters

Parameter Description

norm_table_name

Name of the transformation definition table for linear normalization. You can use the CREATE_NORM_LIN Procedure to create the definition table. The following columns are required:

COL       VARCHAR2(30)
 SHIFT     NUMBER
 SCALE     NUMBER

CREATE_NORM_LIN creates an additional column, ATT, which may be used for specifying nested attributes. This column is not used by INSERT_NORM_LIN_SCALE.

data_table_name

Name of the table containing the data to be transformed

exclude_list

List of NUMBER columns to be excluded from normalization. If you do not specify exclude_list, all NUMBER columns are transformed.

The format of exclude_list is:

dbms_data_mining_transform.COLUMN_LIST('col1','col2',
                                       ...'coln')

round_num

The number of significant digits to use for scale. The default number is 6.

norm_schema_name

Schema of norm_table_name. If no schema is specified, the current schema is used.

data_schema_name

Schema of data_table_name. If no schema is specified, the current schema is used.


Examples

In this example, INSERT_NORM_LIN_SCALE normalizes the cust_year_of_birth column and inserts the transformation in a transformation definition table. The STACK_NORM_LIN Procedure creates a transformation list from the contents of the definition table.

The SQL expression that computes the transformation is shown in the view MINING_DATA_STACK. The view is for display purposes only; it cannot be used to embed the transformations in a model.

CREATE OR REPLACE VIEW mining_data AS
      SELECT cust_id, cust_gender, cust_year_of_birth
      FROM sh.customers;

DESCRIBE mining_data
 Name                               Null?    Type
 ---------------------------------- -------- ------------------
 CUST_ID                            NOT NULL NUMBER
 CUST_GENDER                        NOT NULL CHAR(1)
 CUST_YEAR_OF_BIRTH                 NOT NULL NUMBER(4)
 
BEGIN
   dbms_data_mining_transform.CREATE_NORM_LIN(
       norm_table_name  => 'norm_tbl');
       dbms_data_mining_transform.INSERT_NORM_LIN_SCALE(
       norm_table_name  => 'norm_tbl',
       data_table_name  => 'mining_data',
       exclude_list     => dbms_data_mining_transform.COLUMN_LIST( 'cust_id'),
       round_num        => 3);
 END;
 /
 
SELECT col, shift, scale FROM norm_tbl;
 
COL                  SHIFT SCALE
-------------------- ----- -----
CUST_YEAR_OF_BIRTH       0  1990
 
DECLARE
    xforms      dbms_data_mining_transform.TRANSFORM_LIST;
BEGIN
    dbms_data_mining_transform.STACK_NORM_LIN (
        norm_table_name    => 'norm_tbl',
        xform_list        => xforms);
    dbms_data_mining_transform.XFORM_STACK (
        xform_list         => xforms,
        data_table_name    => 'mining_data',
        xform_view_name    => 'mining_data_stack');
END;
/
 
set long 3000
SELECT text FROM user_views WHERE view_name IN 'MINING_DATA_STACK';
 
TEXT
--------------------------------------------------------------------------------
SELECT "CUST_ID","CUST_GENDER",("CUST_YEAR_OF_BIRTH"-0)/1990 "CUST_YEAR_OF_BIRTH
" FROM mining_data

INSERT_NORM_LIN_ZSCORE Procedure

This procedure performs linear normalization and inserts the transformation definitions in a transformation definition table. INSERT_NORM_LIN_ZSCORE computes the mean and the standard deviation from the data and sets the value of shift and scale as follows:

shift = mean
scale = stddev

Normalization is computed as:

x_new = (x_old - shift)/scale

INSERT_NORM_LIN_ZSCORE rounds the value of scale to a specified number of significant digits before storing it in the transformation definition table.

INSERT_NORM_LIN_ZSCORE normalizes all the NUMBER and FLOAT columns in the data unless you specify a list of columns to ignore.

Syntax

DBMS_DATA_MINING_TRANSFORM.INSERT_NORM_LIN_ZSCORE (
     norm_table_name     IN VARCHAR2,
     data_table_name     IN VARCHAR2,
     exclude_list        IN COLUMN_LIST DEFAULT NULL,
     round_num           IN PLS_INTEGER DEFAULT 6,
     norm_schema_name    IN VARCHAR2 DEFAULT NULL,
     data_schema_name    IN VARCHAR2 DEFAULT NULL);

Parameters

Table 45-31 INSERT_NORM_LIN_ZSCORE Procedure Parameters

Parameter Description

norm_table_name

Name of the transformation definition table for linear normalization. You can use the CREATE_NORM_LIN Procedure to create the definition table. The following columns are required:

COL       VARCHAR2(30)
 SHIFT     NUMBER
 SCALE     NUMBER

CREATE_NORM_LIN creates an additional column, ATT, which may be used for specifying nested attributes. This column is not used by INSERT_NORM_LIN_ZSCORE.

data_table_name

Name of the table containing the data to be transformed

exclude_list

List of NUMBER columns to be excluded from normalization. If you do not specify exclude_list, all NUMBER columns are transformed.

The format of exclude_list is:

dbms_data_mining_transform.COLUMN_LIST('col1','col2',
                                       ...'coln')

round_num

The number of significant digits to use for scale. The default number is 6.

norm_schema_name

Schema of norm_table_name. If no schema is specified, the current schema is used.

data_schema_name

Schema of data_table_name. If no schema is specified, the current schema is used.


Examples

In this example, INSERT_NORM_LIN_ZSCORE normalizes the cust_year_of_birth column and inserts the transformation in a transformation definition table. The STACK_NORM_LIN Procedure creates a transformation list from the contents of the definition table.

The SQL expression that computes the transformation is shown in the view MINING_DATA_STACK. The view is for display purposes only; it cannot be used to embed the transformations in a model.

CREATE OR REPLACE VIEW mining_data AS
       SELECT cust_id, cust_gender, cust_year_of_birth
       FROM sh.customers;
 
DESCRIBE mining_data
 Name                                Null?    Type
 ----------------------------------- -------- --------------------
 CUST_ID                             NOT NULL NUMBER
 CUST_GENDER                         NOT NULL CHAR(1)
 CUST_YEAR_OF_BIRTH                  NOT NULL NUMBER(4)
 
BEGIN
    dbms_data_mining_transform.CREATE_NORM_LIN(
       norm_table_name  => 'norm_tbl');
       dbms_data_mining_transform.INSERT_NORM_LIN_ZSCORE(
       norm_table_name  => 'norm_tbl',
       data_table_name  => 'mining_data',
       exclude_list     => dbms_data_mining_transform.COLUMN_LIST( 'cust_id'),
       round_num        => 3);
END;
/
 
SELECT col, shift, scale FROM norm_tbl;
 
COL                  SHIFT SCALE
-------------------- ----- -----
CUST_YEAR_OF_BIRTH    1960    15
 
DECLARE
    xforms      dbms_data_mining_transform.TRANSFORM_LIST;
BEGIN
    dbms_data_mining_transform.STACK_NORM_LIN (
        norm_table_name    => 'norm_tbl',
        xform_list        => xforms);
    dbms_data_mining_transform.XFORM_STACK (
        xform_list         => xforms,
        data_table_name    => 'mining_data',
        xform_view_name    => 'mining_data_stack');
END;
/
 
set long 3000
SQL> SELECT text FROM user_views WHERE view_name IN 'MINING_DATA_STACK';
 
TEXT
--------------------------------------------------------------------------------
SELECT "CUST_ID","CUST_GENDER",("CUST_YEAR_OF_BIRTH"-1960)/15 "CUST_YEAR_OF_BIRT
H" FROM mining_data

SET_EXPRESSION Procedure

This procedure appends a row to a VARCHAR2 array that stores a SQL expression. The array can be used for specifying a transformation expression that is too long to be used with the SET_TRANSFORM Procedure.

The GET_EXPRESSION Function returns a row in the array.

When you use SET_EXPRESSION to build a transformation expression, you must build a corresponding reverse transformation expression, create a transformation record, and add the transformation record to a transformation list.

Syntax

DBMS_DATA_MINING_TRANSFORM.SET_EXPRESSION (
           expression    IN OUT NOCOPY EXPRESSION_REC,
           chunk                       VARCHAR2 DEFAULT NULL);

Parameters

Table 45-32 SET_EXPRESSION Procedure Parameters

Parameter Description

expression

An expression record (EXPRESSION_REC) that specifies a transformation expression or a reverse transformation expression for an attribute. Each expression record includes a VARCHAR2 array and index fields for specifying upper and lower boundaries within the array.

There are two EXPRESSION_REC fields within a transformation record (TRANSFORM_REC): one for the transformation expression; the other for the reverse transformation expression.

See Table 45-1 for a description of the EXPRESSION_REC type.

chunk

A VARCHAR2 chunk (row) to be appended to expression.


Notes

  1. You can pass NULL in the chunk argument to SET_EXPRESSION to clear the previous chunk. The default value of chunk is NULL.

  2. See "About Transformation Lists".

  3. See "Operational Notes".

Examples

This example shows how you could use SET_EXPRESSION to normalize the CUST_YEAR_OF_BIRTH column and bin the CUST_POSTAL_CODE column.

CREATE OR REPLACE VIEW mining_data AS
     SELECT cust_id, cust_year_of_birth, cust_postal_code, cust_credit_limit
     FROM sh.customers;
 
DECLARE
      v_expr dbms_data_mining_transform.EXPRESSION_REC;
      v_rexp dbms_data_mining_transform.EXPRESSION_REC;
      v_xrec dbms_data_mining_transform.TRANSFORM_REC;
      v_xlst dbms_data_mining_transform.TRANSFORM_LIST :=
                    dbms_data_mining_transform.TRANSFORM_LIST(NULL);
BEGIN
  dbms_data_mining_transform.SET_EXPRESSION(
        EXPRESSION  => v_expr,
        CHUNK       => '("CUST_YEAR_OF_BIRTH"-1910)/77');
  dbms_data_mining_transform.SET_EXPRESSION(
        EXPRESSION  => v_rexp,
        CHUNK       => '"CUST_YEAR_OF_BIRTH"*77+1910');
  v_xrec := null;
  v_xrec.attribute_name := 'CUST_YEAR_OF_BIRTH';
  v_xrec.expression := v_expr;
  v_xrec.reverse_expression := v_rexp;
  v_xlst.TRIM;
  v_xlst.extend(2);
  v_xlst(1) := v_xrec;
 
  v_expr := null;
  v_rexp := null;
  dbms_data_mining_transform.SET_EXPRESSION(
      EXPRESSION  => v_expr,
      CHUNK       => 'DECODE("CUST_POSTAL_CODE",''38082'','1'',
                      '''55787'',''3'',''63736'',''2'',
                      ''78558'',''4'',NULL,NULL,''5'')');
  dbms_data_mining_transform.SET_EXPRESSION(
      EXPRESSION  => v_rexp,
      CHUNK       => 'DECODE("CUST_POSTAL_CODE",''1'',''''38082'''','
                      '''3'',''''55787'''',''2'',''''63736'''', 
                      ''4'',''''78558'''',NULL,''NULL'',''5'',''DEFAULT'')');
  v_xrec := null;
  v_xrec.attribute_name := 'CUST_POSTAL_CODE';
  v_xrec.expression := v_expr;
  v_xrec.reverse_expression := v_rexp;
  v_xlst(2) := v_xrec;

  dbms_data_mining_transform.XFORM_STACK (
      xform_list           =>  v_xlst,
      data_table_name      => 'mining_data',
      xform_view_name      => 'v_xlst_view');
  dbms_output.put_line('====');
      FOR i IN 1..v_xlst.count LOOP
         dbms_output.put_line('ATTR: '||v_xlst(i).attribute_name);
         dbms_output.put_line('SUBN: '||v_xlst(i).attribute_subname);
         dbms_output.put_line('EXPR: '||
         dbms_data_mining_transform.(v_xlst(i).expression));
         dbms_output.put_line('REXP: '||
  dbms_data_mining_transform.(v_xlst(i).reverse_expression));
         dbms_output.put_line('====');
  END LOOP;
END;
/
====
ATTR: CUST_YEAR_OF_BIRTH
SUBN:
EXPR: ("CUST_YEAR_OF_BIRTH"-1910)/77
REXP: "CUST_YEAR_OF_BIRTH"*77+1910
====
ATTR: CUST_POSTAL_CODE
SUBN:
EXPR: DECODE("CUST_POSTAL_CODE",'38082','1','55787','3','63736','2',
             '78558','4',NULL,NULL,'5')
REXP: DECODE("CUST_POSTAL_CODE",'1',''38082'','3',''55787'','2',
             ''63736'','4',''78558'',NULL,'NULL','5','DEFAULT')
====
 
SELECT * FROM v_xlst_view WHERE cust_id BETWEEN 104450 AND 104455 
         ORDER BY cust_id;
 
 CUST_ID CUST_YEAR_OF_BIRTH CUST_POSTAL_CODE     CUST_CREDIT_LIMIT
-------- ------------------ -------------------- -----------------
  104450           .8831169 4                                15000
  104451           .8311688 5                                 1500
  104452           .9350649 5                                11000
  104453           .5714286 5                                11000
  104454           .4285714 5                                 9000
  104455           .8181818 5                                 1500

SET_TRANSFORM Procedure

This procedure appends the transformation instructions for an attribute to a transformation list.

Syntax

DBMS_DATA_MINING_TRANSFORM.SET_TRANSFORM (
          xform_list               IN OUT NOCOPY TRANSFORM_LIST,
          attribute_name           VARCHAR2,
          attribute_subname        VARCHAR2,
          expression               VARCHAR2,
          reverse_expression       VARCHAR2,
          attribute_spec           VARCHAR2 DEFAULT NULL);

Parameters

Table 45-33 SET_TRANSFORM Procedure Parameters

Parameter Description

xform_list

A transformation list. See Table 45-1for a description of the TRANSFORM_LIST object type.

attribute_name

Name of the attribute to be transformed

attribute_subname

Name of the nested attribute if attribute_name is a nested column, otherwise NULL.

expression

A SQL expression that specifies the transformation of the attribute.

reverse_expression

A SQL expression that reverses the transformation for readability in model details and in the target of a supervised model (if the attribute is a target)

attribute_spec

You can specify the value NOPREP for attribute_spec to prevent this attribute from being automatically transformed when Automatic Data Preparation is being used.


Usage Notes

See "Operational Notes". The following sections are especially relevant:

Examples

This example uses SET_TRANSFORM to append transformations to cust_stack for the data set cust_info and displays one row of the transformed data.

SET_TRANSFORM divides the country_id column by 10, removes the cust_year_of_birth column, and multiplies the nested attribute'custprods.mouse pad' by 10. (See "DESCRIBE_STACK Procedure" for the definition of cust_info.)

describe cust_info
 Name                                      Null?    Type
 ----------------------------------------- -------- ----------------------------
 CUST_ID                                   NOT NULL NUMBER
 COUNTRY_ID                                NOT NULL NUMBER
 CUST_YEAR_OF_BIRTH                        NOT NULL NUMBER(4)
 CUSTPRODS                                          SYS.DM_NESTED_NUMERICALS

DECLARE
  cust_stack   dbms_data_mining_transform.TRANSFORM_LIST;
BEGIN
  dbms_data_mining_transform.SET_TRANSFORM (cust_stack,
     'country_id', NULL, 'country_id/10', 'country_id*10');
  dbms_data_mining_transform.SET_TRANSFORM (cust_stack,
      'cust_year_of_birth', NULL, NULL, NULL);
  dbms_data_mining_transform.SET_TRANSFORM (cust_stack,
      'custprods', 'Mouse Pad', 'value*100', 'value/100');
  dbms_data_mining_transform.XFORM_STACK (cust_stack,
     'cust_info', 'xform_cust_view');
END;
/

select * from xform_cust_view where cust_id = 100004;
 
CUST_ID  COUNTRY_ID  CUSTPRODS(ATTRIBUTE_NAME, VALUE)
-------  ----------  ----------------------------------------------
100004         5279  DM_NESTED_NUMERICALS(DM_NESTED_NUMERICAL
                     ('External 8X CD-ROM', 1),
                     DM_NESTED_NUMERICAL('Keyboard Wrist Rest', 1))

STACK_BIN_CAT Procedure

This procedure adds categorical binning transformations to a transformation list.

Syntax

DBMS_DATA_MINING_TRANSFORM.STACK_BIN_CAT (
     bin_table_name     IN            VARCHAR2,
     xform_list         IN OUT NOCOPY TRANSFORM_LIST,
     literal_flag       IN            BOOLEAN  DEFAULT FALSE,
     bin_schema_name    IN            VARCHAR2 DEFAULT NULL);

Parameters

Table 45-34 STACK_BIN_CAT Procedure Parameters

Parameter Description

bin_table_name

Name of the transformation definition table for categorical binning. You can use the CREATE_BIN_CAT Procedure to create the definition table. The table must be populated with transformation definitions before you call STACK_BIN_CAT. To populate the table, you can use one of the INSERT procedures for categorical binning or you can write your own SQL.

See Table 45-4, "Columns in a Transformation Definition Table for Categorical Binning"

xform_list

A transformation list. See Table 45-1 for a description of the TRANSFORM_LIST object type.

literal_flag

Indicates whether the values in the bin column in the transformation definition table are valid SQL literals. When literal_flag is FALSE (the default), the bin identifiers will be transformed to SQL literals by surrounding them with single quotes.

Set literal_flag to TRUE if the bin identifiers are numbers that should have a numeric data type, as is the case for an O-Cluster model.

See "INSERT_BIN_NUM_EQWIDTH Procedure" for an example.

bin_schema_name

Schema of bin_table_name. If no schema is specified, the current schema is used.


Usage Notes

See "Operational Notes". The following sections are especially relevant:

Examples

This example shows how a binning transformation for the categorical column cust_postal_code could be added to a stack called mining_data_stack.

Note:

This example invokes the XFORM_STACK Procedure to show how the data is transformed by the stack. XFORM_STACK simply generates an external view of the transformed data. The actual purpose of the STACK procedures is to assemble a list of transformations for embedding in a model. The transformations are passed to CREATE_MODEL in the xform_list parameter. See INSERT_BIN_NUM_EQWIDTH Procedure for an example.
CREATE or REPLACE VIEW mining_data AS
    SELECT cust_id, cust_postal_code, cust_credit_limit
       FROM sh.customers
       WHERE cust_id BETWEEN 100050 AND 100100;
BEGIN
   dbms_data_mining_transform.CREATE_BIN_CAT ('bin_cat_tbl');
   dbms_data_mining_transform.INSERT_BIN_CAT_FREQ (
        bin_table_name   => 'bin_cat_tbl',
        data_table_name  => 'mining_data',
        bin_num          =>  3);
  END;
/
DECLARE
  MINING_DATA_STACK   dbms_data_mining_transform.TRANSFORM_LIST;
BEGIN
  dbms_data_mining_transform.STACK_BIN_CAT (
    bin_table_name       => 'bin_cat_tbl',
    xform_list           =>  mining_data_stack);
  dbms_data_mining_transform.XFORM_STACK (
    xform_list           =>  mining_data_stack,
    data_table_name      => 'mining_data',
    xform_view_name      => 'mining_data_stack_view');
  END;
/
-- Before transformation
column cust_postal_code format a16
SELECT * from mining_data 
              WHERE cust_id BETWEEN 100050 AND 100053
              ORDER BY cust_id;

  CUST_ID CUST_POSTAL_CODE CUST_CREDIT_LIMIT
---------- ---------------- -----------------
    100050 76486                         1500
    100051 73216                         9000
    100052 69499                         5000
    100053 45704                         7000

-- After transformation
SELECT * FROM mining_data_stack_view
              WHERE cust_id BETWEEN 100050 AND 100053
              ORDER BY cust_id;
 
  CUST_ID CUST_POSTAL_CODE CUST_CREDIT_LIMIT
---------- ---------------- -----------------
    100050 4                             1500
    100051 1                             9000
    100052 4                             5000
    100053 4                             7000

STACK_BIN_NUM Procedure

This procedure adds numerical binning transformations to a transformation list.

Syntax

DBMS_DATA_MINING_TRANSFORM.STACK_BIN_NUM (
     bin_table_name     IN             VARCHAR2,
     xform_list         IN OUT  NOCOPY TRANSFORM_LIST,
     literal_flag       IN             BOOLEAN  DEFAULT FALSE,
     bin_schema_name    IN             VARCHAR2 DEFAULT NULL);

Parameters

Table 45-35 STACK_BIN_NUM Procedure Parameters

Parameter Description

bin_table_name

Name of the transformation definition table for numerical binning. You can use the CREATE_BIN_NUM Procedure to create the definition table. The table must be populated with transformation definitions before you call STACK_BIN_NUM. To populate the table, you can use one of the INSERT procedures for numerical binning or you can write your own SQL.

See Table 45-6, "Columns in a Transformation Definition Table for Numerical Binning".

xform_list

A transformation list. See Table 45-1 for a description of the TRANSFORM_LIST object type.

literal_flag

Indicates whether the values in the bin column in the transformation definition table are valid SQL literals. When literal_flag is FALSE (the default), the bin identifiers will be transformed to SQL literals by surrounding them with single quotes.

Set literal_flag to TRUE if the bin identifiers are numbers that should have a numeric data type, as is the case for an O-Cluster model.

See "INSERT_BIN_NUM_EQWIDTH Procedure" for an example.

bin_schema_name

Schema of bin_table_name. If no schema is specified, the current schema is used.


Usage Notes

See "Operational Notes". The following sections are especially relevant:

Examples

This example shows how a binning transformation for the numerical column cust_credit_limit could be added to a stack called mining_data_stack.

Note:

This example invokes the XFORM_STACK Procedure to show how the data is transformed by the stack. XFORM_STACK simply generates an external view of the transformed data. The actual purpose of the STACK procedures is to assemble a list of transformations for embedding in a model. The transformations are passed to CREATE_MODEL in the xform_list parameter. See INSERT_BIN_NUM_EQWIDTH Procedure for an example.
CREATE OR REPLACE VIEW mining_data AS
    SELECT cust_id, cust_postal_code, cust_credit_limit
      FROM sh.customers
      WHERE cust_id BETWEEN 100050 and 100100;
BEGIN
  dbms_data_mining_transform.create_bin_num ('bin_num_tbl');
  dbms_data_mining_transform.insert_bin_num_qtile (
  bin_table_name    => 'bin_num_tbl',
  data_table_name   => 'mining_data',
  bin_num                   => 5,
  exclude_list      =>  dbms_data_mining_transform.COLUMN_LIST('cust_id'));
END;
/
DECLARE
   MINING_DATA_STACK   dbms_data_mining_transform.TRANSFORM_LIST;
BEGIN
   dbms_data_mining_transform.STACK_BIN_CAT (
      bin_table_name          => 'bin_num_tbl',
      xform_list         =>  mining_data_stack);
   dbms_data_mining_transform.XFORM_STACK (
      xform_list         =>  mining_data_stack,
      data_table_name   => 'mining_data',
      xform_view_name   => 'mining_data_stack_view');
END;
/
-- Before transformation
SELECT cust_id, cust_postal_code, ROUND(cust_credit_limit) FROM mining_data
   WHERE cust_id BETWEEN 100050 AND 100055
   ORDER BY cust_id;
CUST_ID   CUST_POSTAL_CODE   ROUND(CUST_CREDIT_LIMIT)
-------   -----------------  -------------------------
100050    76486                                 1500
100051    73216                                 9000 
100052    69499                                 5000
100053    45704                                 7000
100055    74673                                11000
100055    74673                                11000

-- After transformation
SELECT cust_id, cust_postal_code, ROUND(cust_credit_limit)
   FROM mining_data_stack_view
   WHERE cust_id BETWEEN 100050 AND 100055
   ORDER BY cust_id;
CUST_ID   CUST_POSTAL_CODE   ROUND(CUST_CREDIT_LIMITT)
-------   ----------------   -------------------------
100050    76486                                     
100051    73216                                   2
100052    69499                                   1
100053    45704                                   
100054    88021                                   3
100055    74673                                   3

STACK_CLIP Procedure

This procedure adds clipping transformations to a transformation list.

Syntax

DBMS_DATA_MINING_TRANSFORM.STACK_CLIP (
          clip_table_name     IN            VARCHAR2,
          xform_list          IN OUT NOCOPY TRANSFORM_LIST,
          clip_schema_name    IN            VARCHAR2 DEFAULT NULL);

Parameters

Table 45-36 STACK_CLIP Procedure Parameters

Parameter Description

clip_table_name

Name of the transformation definition table for clipping.You can use the CREATE_CLIP Procedure to create the definition table. The table must be populated with transformation definitions before you call STACK_CLIP. To populate the table, you can use one of the INSERT procedures for clipping or you can write your own SQL.

See Table 45-8, "Columns in a Transformation Definition Table for Clipping or Winsorizing"

xform_list

A transformation list. See Table 45-1 for a description of the TRANSFORM_LIST object type.

clip_schema_name

Schema of clip_table_name. If no schema is specified, the current schema is used.


Usage Notes

See "Operational Notes". The following sections are especially relevant:

Examples

This example shows how a clipping transformation for the numerical column cust_credit_limit could be added to a stack called mining_data_stack.

Note:

This example invokes the XFORM_STACK Procedure to show how the data is transformed by the stack. XFORM_STACK simply generates an external view of the transformed data. The actual purpose of the STACK procedures is to assemble a list of transformations for embedding in a model. The transformations are passed to CREATE_MODEL in the xform_list parameter. See INSERT_BIN_NUM_EQWIDTH Procedure for an example.
CREATE OR REPLACE VIEW mining_data AS
       SELECT cust_id, cust_postal_code, cust_credit_limit
       FROM sh.customers
       WHERE cust_id BETWEEN 100050 AND 100100;
BEGIN
   dbms_data_mining_transform.create_clip ('clip_tbl');
   dbms_data_mining_transform.insert_clip_winsor_tail (
       clip_table_name   => 'clip_tbl',
       data_table_name   => 'mining_data',
       tail_frac         => 0.25,
       exclude_list      => dbms_data_mining_transform.COLUMN_LIST('cust_id'));
END;
/
DECLARE
      MINING_DATA_STACK   dbms_data_mining_transform.TRANSFORM_LIST;
BEGIN
      dbms_data_mining_transform.STACK_CLIP (
         clip_table_name     => 'clip_tbl',
         xform_list          =>  mining_data_stack);
      dbms_data_mining_transform.XFORM_STACK (
          xform_list        =>  mining_data_stack,
          data_table_name   => 'mining_data',
          xform_view_name   => 'mining_data_stack_view');
END;
/
-- Before transformation
SELECT cust_id, cust_postal_code, round(cust_credit_limit) 
  FROM mining_data
    WHERE cust_id BETWEEN 100050 AND 100054
    ORDER BY cust_id;
    
CUST_ID   CUST_POSTAL_CODE   ROUND(CUST_CREDIT_LIMIT)
-------   ----------------   ------------------------ 
100050    76486                                 1500 
100051    73216                                 9000 
100052    69499                                 5000 
100053    45704                                 7000 
100054    88021                                11000 
 
-- After transformation
SELECT cust_id, cust_postal_code, round(cust_credit_limit)  
  FROM mining_data_stack_view
    WHERE cust_id BETWEEN 100050 AND 100054
    ORDER BY cust_id;
    
CUST_ID   CUST_POSTAL_CODE   ROUND(CUST_CREDIT_LIMIT)
-------   ----------------   ------------------------
100050    76486                                 5000
100051    73216                                 9000
100052    69499                                 5000
100053    45704                                 7000
100054    88021                                11000

STACK_COL_REM Procedure

This procedure adds column removal transformations to a transformation list.

Syntax

DBMS_DATA_MINING_TRANSFORM.STACK_COL_REM (
     rem_table_name     IN            VARCHAR2,
     xform_list         IN OUT NOCOPY TRANSFORM_LIST,
     rem_schema_name    IN            VARCHAR2 DEFAULT NULL);

Parameters

Table 45-37 STACK_COL_REM Procedure Parameters

Parameter Description

rem_table_name

Name of the transformation definition table for column removal. You can use the CREATE_COL_REM Procedure to create the definition table. See Table 45-10, "Columns in a Transformation Definition Table for Column Removal".

The table must be populated with column names before you call STACK_COL_REM. The INSERT_BIN_SUPER Procedure and the INSERT_AUTOBIN_NUM_EQWIDTH Procedure can optionally be used to populate the table. You can also use SQL INSERT statements.

xform_list

A transformation list. See Table 45-1 for a description of the TRANSFORM_LIST object type.

rem_schema_name

Schema of rem_table_name. If no schema is specified, the current schema is used.


Usage Notes

See "Operational Notes". The following sections are especially relevant:

Examples

This example shows how the column cust_credit_limit could be removed in a transformation list called mining_data_stack.

Note:

This example invokes the XFORM_STACK Procedure to show how the data is transformed by the stack. XFORM_STACK simply generates an external view of the transformed data. The actual purpose of the STACK procedures is to assemble a list of transformations for embedding in a model. The transformations are passed to CREATE_MODEL in the xform_list parameter. See INSERT_BIN_NUM_EQWIDTH Procedure for an example.
CREATE OR REPLACE VIEW mining_data AS
     SELECT cust_id, country_id, cust_postal_code, cust_credit_limit
        FROM sh.customers;
 
BEGIN
    dbms_data_mining_transform.create_col_rem ('rem_tbl');
END;
/

INSERT into rem_tbl VALUES (upper('cust_postal_code'), null);
 
DECLARE
  MINING_DATA_STACK   dbms_data_mining_transform.TRANSFORM_LIST;
BEGIN
     dbms_data_mining_transform.stack_col_rem (
          rem_table_name     => 'rem_tbl',
          xform_list         =>  mining_data_stack);
      dbms_data_mining_transform.XFORM_STACK (
          xform_list         =>  mining_data_stack,
          data_table_name    => 'mining_data',
          xform_view_name    => 'mining_data_stack_view');
END;
/
 
SELECT *  FROM mining_data
  WHERE cust_id BETWEEN 100050 AND 100051
  ORDER BY cust_id;
 
CUST_ID   COUNTRY_ID   CUST_POSTAL_CODE   CUST_CREDIT_LIMIT
-------   ----------   ----------------   -----------------
100050         52773   76486                          1500
100051         52790   73216                          9000
 
SELECT *  FROM mining_data_stack_view
  WHERE cust_id BETWEEN 100050 AND 100051
  ORDER BY cust_id;
 
CUST_ID   COUNTRY_ID   CUST_CREDIT_LIMIT
-------   ----------   -----------------
100050         52773              1500
100051         52790              9000

STACK_MISS_CAT Procedure

This procedure adds categorical missing value transformations to a transformation list.

Syntax

DBMS_DATA_MINING_TRANSFORM.STACK_MISS_CAT (
     miss_table_name     IN       VARCHAR2,
     xform_list          IN OUT   NOCOPY TRANSFORM_LIST,
     miss_schema_name    IN       VARCHAR2 DEFAULT NULL);

Parameters

Table 45-38 STACK_MISS_CAT Procedure Parameters

Parameter Description

miss_table_name

Name of the transformation definition table for categorical missing value treatment. You can use the CREATE_MISS_CAT Procedure to create the definition table. The table must be populated with transformation definitions before you call STACK_MISS_CAT. To populate the table, you can use the INSERT_MISS_CAT_MODE Procedure or you can write your own SQL.

See Table 45-12, "Columns in a Transformation Definition Table for Categorical Missing Value Treatment".

xform_list

A transformation list. See Table 45-1 for a description of the TRANSFORM_LIST object type.

miss_schema_name

Schema of miss_table_name. If no schema is specified, the current schema is used.


Usage Notes

See "Operational Notes". The following sections are especially relevant:

Examples

This example shows how the missing values in the column cust_marital_status could be replaced with the mode in a transformation list called mining_data_stack.

Note:

This example invokes the XFORM_STACK Procedure to show how the data is transformed by the stack. XFORM_STACK simply generates an external view of the transformed data. The actual purpose of the STACK procedures is to assemble a list of transformations for embedding in a model. The transformations are passed to CREATE_MODEL in the xform_list parameter. See INSERT_BIN_NUM_EQWIDTH Procedure for an example.
CREATE OR REPLACE VIEW mining_data AS
      SELECT cust_id, country_id, cust_marital_status
         FROM sh.customers
         where cust_id BETWEEN 1 AND 10;
 
BEGIN
  dbms_data_mining_transform.create_miss_cat ('miss_cat_tbl');
  dbms_data_mining_transform.insert_miss_cat_mode ('miss_cat_tbl', 'mining_data');
END;
/
 
DECLARE
  MINING_DATA_STACK   dbms_data_mining_transform.TRANSFORM_LIST;
BEGIN
     dbms_data_mining_transform.stack_miss_cat (
          miss_table_name   => 'miss_cat_tbl',
          xform_list        => mining_data_stack);
      dbms_data_mining_transform.XFORM_STACK (
          xform_list        =>  mining_data_stack,
          data_table_name   => 'mining_data',
          xform_view_name   => 'mining_data_stack_view');
END;
/
SELECT * FROM mining_data
  ORDER BY cust_id;
 
CUST_ID   COUNTRY_ID   CUST_MARITAL_STATUS
-------   ----------   --------------------
      1      52789
      2      52778
      3      52770
      4      52770
      5      52789
      6      52769     single
      7      52790     single
      8      52790     married
      9      52770     divorced
     10      52790     widow
 
SELECT * FROM mining_data_stack_view
   ORDER By cust_id;
 
CUST_ID   COUNTRY_ID   CUST_MARITAL_STATUS
-------   -----------  --------------------
      1       52789    single
      2       52778    single
      3       52770    single
      4       52770    single
      5       52789    single
      6       52769    single
      7       52790    single
      8       52790    married
      9       52770    divorced
     10       52790    widow

STACK_MISS_NUM Procedure

This procedure adds numeric missing value transformations to a transformation list.

Syntax

DBMS_DATA_MINING_TRANSFORM.STACK_MISS_NUM (
     miss_table_name     IN       VARCHAR2,
     xform_list          IN OUT   NOCOPY TRANSFORM_LIST,
     miss_schema_name    IN       VARCHAR2 DEFAULT NULL);

Parameters

Table 45-39 STACK_MISS_NUM Procedure Parameters

Parameter Description

miss_table_name

Name of the transformation definition table for numerical missing value treatment. You can use the CREATE_MISS_NUM Procedure to create the definition table. The table must be populated with transformation definitions before you call STACK_MISS_NUM. To populate the table, you can use the INSERT_MISS_NUM_MEAN Procedure or you can write your own SQL.

See Table 45-14, "Columns in a Transformation Definition Table for Numerical Missing Value Treatment".

xform_list

A transformation list. See Table 45-1 for a description of the TRANSFORM_LIST object type.

miss_schema_name

Schema of miss_table_name. If no schema is specified, the current schema is used.


Usage Notes

See "Operational Notes". The following sections are especially relevant:

Examples

This example shows how the missing values in the column cust_credit_limit could be replaced with the mean in a transformation list called mining_data_stack.

Note:

This example invokes the XFORM_STACK Procedure to show how the data is transformed by the stack. XFORM_STACK simply generates an external view of the transformed data. The actual purpose of the STACK procedures is to assemble a list of transformations for embedding in a model. The transformations are passed to CREATE_MODEL in the xform_list parameter. See INSERT_BIN_NUM_EQWIDTH Procedure for an example.
describe mining_data
 Name                                                  Null?    Type
 ----------------------------------------------------- -------- -----
 CUST_ID                                               NOT NULL NUMBER
 CUST_CREDIT_LIMIT                                              NUMBER

BEGIN
   dbms_data_mining_transform.create_miss_num ('miss_num_tbl');
   dbms_data_mining_transform.insert_miss_num_mean ('miss_num_tbl','mining_data');
END;
/
SELECT * FROM miss_num_tbl;
 
COL                  ATT      VAL
-------------------- ----- ------
CUST_ID                       5.5
CUST_CREDIT_LIMIT          185.71
 
DECLARE
    MINING_DATA_STACK   dbms_data_mining_transform.TRANSFORM_LIST;
  BEGIN
    dbms_data_mining_transform.STACK_MISS_NUM (
          miss_table_name   => 'miss_num_tbl',
          xform_list        => mining_data_stack);
    dbms_data_mining_transform.XFORM_STACK (
          xform_list        =>  mining_data_stack,
          data_table_name   => 'mining_data',
          xform_view_name   => 'mining_data_stack_view');
END;
/
-- Before transformation
SELECT * FROM mining_data
  ORDER BY cust_id;
CUST_ID CUST_CREDIT_LIMIT
------- -----------------
      1               100
      2
      3               200
      4
      5               150
      6               400
      7               150
      8
      9               100
     10               200
 
-- After transformation
SELECT * FROM mining_data_stack_view
  ORDER BY cust_id;
CUST_ID CUST_CREDIT_LIMIT
------- -----------------
      1               100
      2            185.71
      3               200
      4            185.71
      5               150
      6               400
      7               150
      8            185.71
      9               100
     10               200

STACK_NORM_LIN Procedure

This procedure adds linear normalization transformations to a transformation list.

Syntax

DBMS_DATA_MINING_TRANSFORM.STACK_NORM_LIN (
     norm_table_name     IN       VARCHAR2,
     xform_list          IN OUT   NOCOPY TRANSFORM_LIST,
     norm_schema_name    IN       VARCHAR2 DEFAULT NULL);

Parameters

Table 45-40 STACK_NORM_LIN Procedure Parameters

Parameter Description

norm_table_name

Name of the transformation definition table for linear normalization. You can use the CREATE_NORM_LIN Procedure to create the definition table. The table must be populated with transformation definitions before you call STACK_NORM_LIN.To populate the table, you can use one of the INSERT procedures for normalization or you can write your own SQL.

See Table 45-16, "Columns in a Transformation Definition Table for Linear Normalization".

xform_list

A transformation list. See Table 45-1 for a description of the TRANSFORM_LIST object type.

norm_schema_name

Schema of norm_table_name. If no schema is specified, the current schema is used.


Usage Notes

See "Operational Notes". The following sections are especially relevant:

Examples

This example shows how the column cust_credit_limit could be normalized in a transformation list called mining_data_stack.

Note:

This example invokes the XFORM_STACK Procedure to show how the data is transformed by the stack. XFORM_STACK simply generates an external view of the transformed data. The actual purpose of the STACK procedures is to assemble a list of transformations for embedding in a model. The transformations are passed to CREATE_MODEL in the xform_list parameter. See INSERT_BIN_NUM_EQWIDTH Procedure for an example.
CREATE OR REPLACE VIEW mining_data AS
       SELECT cust_id, country_id, cust_postal_code, cust_credit_limit
          FROM sh.customers;
BEGIN
   dbms_data_mining_transform.create_norm_lin ('norm_lin_tbl');
   dbms_data_mining_transform.insert_norm_lin_minmax (
       norm_table_name   => 'norm_lin_tbl',
       data_table_name   => 'mining_data',
       exclude_list      =>  dbms_data_mining_transform.COLUMN_LIST('cust_id',
                                                             'country_id'));
END;
/
SELECT * FROM norm_lin_tbl;
COL                  ATT    SHIFT  SCALE
-------------------- ----- ------ ------
CUST_CREDIT_LIMIT            1500  13500

DECLARE
   MINING_DATA_STACK   dbms_data_mining_transform.TRANSFORM_LIST;
BEGIN
   dbms_data_mining_transform.stack_norm_lin (
        norm_table_name   => 'norm_lin_tbl',
        xform_list        => mining_data_stack);
   dbms_data_mining_transform.XFORM_STACK (
        xform_list        =>  mining_data_stack,
        data_table_name   => 'mining_data',
        xform_view_name   => 'mining_data_stack_view');
END;
/
SELECT * FROM mining_data
  WHERE cust_id between 1 and 10
  ORDER BY cust_id;
CUST_ID COUNTRY_ID CUST_POSTAL_CODE     CUST_CREDIT_LIMIT
------- ---------- -------------------- -----------------
      1      52789 30828                             9000
      2      52778 86319                            10000
      3      52770 88666                             1500
      4      52770 87551                             1500
      5      52789 59200                             1500
      6      52769 77287                             1500
      7      52790 38763                             1500
      8      52790 58488                             3000
      9      52770 63033                             3000
     10      52790 52602                             3000
 
SELECT * FROM mining_data_stack_view
  WHERE cust_id between 1 and 10
  ORDER BY cust_id;
CUST_ID COUNTRY_ID CUST_POSTAL_CODE     CUST_CREDIT_LIMIT
------- ---------- -------------------- -----------------
      1      52789 30828                           .55556
      2      52778 86319                           .62963
      3      52770 88666                                0
      4      52770 87551                                0
      5      52789 59200                                0
      6      52769 77287                                0
      7      52790 38763                                0
      8      52790 58488                           .11111
      9      52770 63033                           .11111
     10      52790 52602                           .11111

XFORM_BIN_CAT Procedure

This procedure creates a view that implements the categorical binning transformations specified in a definition table. Only the columns that are specified in the definition table are transformed; the remaining columns from the data table are present in the view, but they are not changed.

Syntax

DBMS_DATA_MINING_TRANSFORM.XFORM_BIN_CAT (
     bin_table_name       IN VARCHAR2,
     data_table_name      IN VARCHAR2,
     xform_view_name      IN VARCHAR2,
     literal_flag         IN BOOLEAN DEFAULT FALSE,
     bin_schema_name      IN VARCHAR2 DEFAULT NULL,
     data_schema_name     IN VARCHAR2 DEFAULT NULL,
     xform_schema_name    IN VARCHAR2 DEFAULT NULL);

Parameters

Table 45-41 XFORM_BIN_CAT Procedure Parameters

Parameter Description

bin_table_name

Name of the transformation definition table for categorical binning. You can use the CREATE_BIN_CAT Procedure to create the definition table. The table must be populated with transformation definitions before you call XFORM_BIN_CAT. To populate the table, you can use one of the INSERT procedures for categorical binning or you can write your own SQL.

See Table 45-4, "Columns in a Transformation Definition Table for Categorical Binning".

data_table_name

Name of the table containing the data to be transformed.

xform_view_name

Name of the view to be created. The view presents columns in data_table_name with the transformations specified in bin_table_name.

literal_flag

Indicates whether the values in the bin column in the transformation definition table are valid SQL literals. When literal_flag is FALSE (the default), the bin identifiers will be transformed to SQL literals by surrounding them with single quotes.

Set literal_flag to TRUE if the bin identifiers are numbers that should have a numeric data type, as is the case for an O-Cluster model.

See "INSERT_BIN_NUM_EQWIDTH Procedure" for an example.

bin_schema_name

Schema of bin_table_name. If no schema is specified, the current schema is used.

data_schema_name

Schema of data_table_name. If no schema is specified, the current schema is used.

xform_schema_name

Schema of xform_view_name. If no schema is specified, the current schema is used.


Usage Notes

See "Operational Notes".

Examples

This example creates a view that bins the cust_postal_code column. The data source consists of three columns from sh.customer.

describe mining_data
 Name                                   Null?    Type
 -------------------------------------- -------- ------------------------
 CUST_ID                                NOT NULL NUMBER
 CUST_POSTAL_CODE                       NOT NULL VARCHAR2(10)
 CUST_CREDIT_LIMIT                               NUMBER
 
SELECT * FROM mining_data WHERE cust_id between 104066 and 104069;
 
   CUST_ID CUST_POSTAL_CODE     CUST_CREDIT_LIMIT                                                   
--------- -------------------- -----------------                                                   
    104066 69776                             7000                                                   
    104067 52602                             9000                                                   
    104068 55787                            11000                                                   
    104069 55977                             5000                                                   
 
BEGIN
  dbms_data_mining_transform.create_bin_cat(
     bin_table_name     => 'bin_cat_tbl');
  dbms_data_mining_transform.insert_bin_cat_freq(
     bin_table_name     => 'bin_cat_tbl',
     data_table_name    => 'mining_data',
     bin_num           => 10);
   dbms_data_mining_transform.xform_bin_cat(
     bin_table_name     => 'bin_cat_tbl',
     data_table_name    => 'mining_data',
     xform_view_name    => 'bin_cat_view');
END;
/
 
SELECT * FROM bin_cat_view WHERE cust_id between 104066 and 104069;
 
   CUST_ID CUST_POSTAL_CODE     CUST_CREDIT_LIMIT                                                   
---------- -------------------- -----------------                                                   
    104066 6                                 7000                                                   
    104067 11                                9000                                                   
    104068 3                                11000                                                   
    104069 11                                5000                                                   
 
SELECT text FROM user_views WHERE view_name IN 'BIN_CAT_VIEW';
 
TEXT                                                                                                
--------------------------------------------------------------------------------                    
SELECT "CUST_ID",DECODE("CUST_POSTAL_CODE",'38082','1','45704','9','48346','5','                    
55787','3','63736','2','67843','7','69776','6','72860','10','78558','4','80841',                    
'8',NULL,NULL,'11') "CUST_POSTAL_CODE","CUST_CREDIT_LIMIT" FROM mining_data                         

XFORM_BIN_NUM Procedure

This procedure creates a view that implements the numerical binning transformations specified in a definition table. Only the columns that are specified in the definition table are transformed; the remaining columns from the data table are present in the view, but they are not changed.

Syntax

DBMS_DATA_MINING_TRANSFORM.XFORM_BIN_NUM (
     bin_table_name     IN VARCHAR2,
     data_table_name    IN VARCHAR2,
     xform_view_name    IN VARCHAR2,
     literal_flag       IN BOOLEAN DEFAULT FALSE,
     bin_schema_name    IN VARCHAR2 DEFAULT NULL,
     data_schema_name   IN VARCHAR2 DEFAULT NULL,
     xform_schema_name  IN VARCHAR2 DEFAULT NULL);

Parameters

Table 45-42 XFORM_BIN_NUM Procedure Parameters

Parameter Description

bin_table_name

Name of the transformation definition table for numerical binning. You can use the CREATE_BIN_NUM Procedure to create the definition table. The table must be populated with transformation definitions before you call XFORM_BIN_NUM. To populate the table, you can use one of the INSERT procedures for numerical binning or you can write your own SQL.

See "Columns in a Transformation Definition Table for Numerical Binning".

data_table_name

Name of the table containing the data to be transformed

xform_view_name

Name of the view to be created. The view presents columns in data_table_name with the transformations specified in bin_table_name.

literal_flag

Indicates whether the values in the bin column in the transformation definition table are valid SQL literals. When literal_flag is FALSE (the default), the bin identifiers will be transformed to SQL literals by surrounding them with single quotes.

Set literal_flag to TRUE if the bin identifiers are numbers that should have a numeric data type, as is the case for an O-Cluster model.

See "INSERT_BIN_NUM_EQWIDTH Procedure" for an example.

bin_schema_name

Schema of bin_table_name. If no schema is specified, the current schema is used.

data_schema_name

Schema of data_table_name. If no schema is specified, the current schema is used.

xform_schema_name

Schema of xform_view_name. If no schema is specified, the current schema is used.


Usage Notes

See "Operational Notes".

Examples

This example creates a view that bins the cust_credit_limit column. The data source consists of three columns from sh.customer.

describe mining_data
 Name                                   Null?    Type
 -------------------------------------- -------- ------------------------
 CUST_ID                                NOT NULL NUMBER
 CUST_POSTAL_CODE                       NOT NULL VARCHAR2(10)
 CUST_CREDIT_LIMIT                               NUMBER
 
column cust_credit_limit off
SELECT * FROM mining_data WHERE cust_id between 104066 and 104069;
 
   CUST_ID CUST_POSTAL_CODE   CUST_CREDIT_LIMIT                                         
---------  ------------------  --------------------                                        
    104066 69776                           7000                                         
    104067 52602                           9000                                         
    104068 55787                          11000                                         
    104069 55977                           5000                                         
 
BEGIN
   dbms_data_mining_transform.create_bin_num(
           bin_table_name     => 'bin_num_tbl');
   dbms_data_mining_transform.insert_autobin_num_eqwidth(
           bin_table_name     => 'bin_num_tbl',
           data_table_name    => 'mining_data',
           bin_num                   => 5,
           max_bin_num            => 10,
           exclude_list       => dbms_data_mining_transform.COLUMN_LIST('cust_id'));
  dbms_data_mining_transform.xform_bin_num(
           bin_table_name      => 'bin_num_tbl',
           data_table_name     => 'mining_data',
           xform_view_name     => 'mining_data_view');
END;
/
describe mining_data_view
 Name                                 Null?    Type
 ------------------------------------ -------- ------------------------
 CUST_ID                              NOT NULL NUMBER
 CUST_POSTAL_CODE                     NOT NULL VARCHAR2(10)
 CUST_CREDIT_LIMIT                             VARCHAR2(2)
 
col cust_credit_limit on
col cust_credit_limit format a25
SELECT * FROM mining_data_view WHERE cust_id between 104066 and 104069;
 
   CUST_ID CUST_POSTAL_CODE     CUST_CREDIT_LIMIT                                         
---------- -------------------- -------------------------                                 
    104066 69776                5                                                         
    104067 52602                6                                                         
    104068 55787                8                                                         
    104069 55977                3                                                         
 
set long 2000
SELECT text FROM user_views WHERE view_name IN 'MINING_DATA_VIEW';
 
TEXT                                                                                      
--------------------------------------------------------------------------------          
SELECT "CUST_ID","CUST_POSTAL_CODE",CASE WHEN "CUST_CREDIT_LIMIT"<1500 THEN NULL          
 WHEN "CUST_CREDIT_LIMIT"<=2850 THEN '1' WHEN "CUST_CREDIT_LIMIT"<=4200 THEN '2'          
 WHEN "CUST_CREDIT_LIMIT"<=5550 THEN '3' WHEN "CUST_CREDIT_LIMIT"<=6900 THEN '4'          
 WHEN "CUST_CREDIT_LIMIT"<=8250 THEN '5' WHEN "CUST_CREDIT_LIMIT"<=9600 THEN '6'          
 WHEN "CUST_CREDIT_LIMIT"<=10950 THEN '7' WHEN "CUST_CREDIT_LIMIT"<=12300 THEN '          
8' WHEN "CUST_CREDIT_LIMIT"<=13650 THEN '9' WHEN "CUST_CREDIT_LIMIT"<=15000 THEN          
 '10' END "CUST_CREDIT_LIMIT" FROM mining_data                                            

XFORM_CLIP Procedure

This procedure creates a view that implements the clipping transformations specified in a definition table. Only the columns that are specified in the definition table are transformed; the remaining columns from the data table are present in the view, but they are not changed.

Syntax

DBMS_DATA_MINING_TRANSFORM.XFORM_CLIP (
    clip_table_name       IN VARCHAR2, 
    data_table_name       IN VARCHAR2,
    xform_view_name       IN VARCHAR2,
    clip_schema_name      IN VARCHAR2 DEFAULT NULL,
    data_schema_name      IN VARCHAR2,DEFAULT NULL,
    xform_schema_name     IN VARCHAR2,DEFAULT NULL);

Parameters

Table 45-43 XFORM_CLIP Procedure Parameters

Parameter Description

clip_table_name

Name of the transformation definition table for clipping. You can use the CREATE_CLIP Procedure to create the definition table. The table must be populated with transformation definitions before you call XFORM_CLIP. To populate the table, you can use one of the INSERT procedures for clipping you can write your own SQL.

See Table 45-8, "Columns in a Transformation Definition Table for Clipping or Winsorizing".

data_table_name

Name of the table containing the data to be transformed

xform_view_name

Name of the view to be created. The view presents columns in data_table_name with the transformations specified in clip_table_name.

clip_schema_name

Schema of clip_table_name. If no schema is specified, the current schema is used.

data_schema_name

Schema of data_table_name. If no schema is specified, the current schema is used.

xform_schema_name

Schema of xform_view_name. If no schema is specified, the current schema is used.


Examples

This example creates a view that clips the cust_credit_limit column. The data source consists of three columns from sh.customer.

describe mining_data
 Name                           Null?    Type
 ------------------------------ -------- -------------------------
 CUST_ID                        NOT NULL NUMBER
 CUST_POSTAL_CODE               NOT NULL VARCHAR2(10)
 CUST_CREDIT_LIMIT                        NUMBER
 
BEGIN
   dbms_data_mining_transform.create_clip(
      clip_table_name    => 'clip_tbl');
   dbms_data_mining_transform.insert_clip_trim_tail(
      clip_table_name   => 'clip_tbl',
      data_table_name   => 'mining_data',
      tail_frac         => 0.05,
      exclude_list      => dbms_data_mining_transform.COLUMN_LIST('cust_id'));
   dbms_data_mining_transform.xform_clip(
      clip_table_name     => 'clip_tbl',
      data_table_name    => 'mining_data',
      xform_view_name    => 'clip_view');
END;
/
describe clip_view
 Name                          Null?    Type
 ----------------------------- -------- --------------------------
 CUST_ID                       NOT NULL NUMBER
 CUST_POSTAL_CODE              NOT NULL VARCHAR2(10)
 CUST_CREDIT_LIMIT                      NUMBER
 
SELECT MIN(cust_credit_limit), MAX(cust_credit_limit) FROM mining_data;
 
MIN(CUST_CREDIT_LIMIT) MAX(CUST_CREDIT_LIMIT)
---------------------- ----------------------
                  1500                  15000
 
SELECT MIN(cust_credit_limit), MAX(cust_credit_limit) FROM clip_view;
 
MIN(CUST_CREDIT_LIMIT) MAX(CUST_CREDIT_LIMIT)
---------------------- ----------------------
                  1500                  11000
 
set long 2000
SELECT text FROM user_views WHERE view_name IN 'CLIP_VIEW';
 
TEXT
--------------------------------------------------------------------------------
SELECT "CUST_ID","CUST_POSTAL_CODE",CASE WHEN "CUST_CREDIT_LIMIT" < 1500 THEN NU
LL WHEN "CUST_CREDIT_LIMIT" > 11000 THEN NULL ELSE "CUST_CREDIT_LIMIT" END "CUST
_CREDIT_LIMIT" FROM mining_data

XFORM_COL_REM Procedure

This procedure creates a view that implements the column removal transformations specified in a definition table. Only the columns that are specified in the definition table are removed; the remaining columns from the data table are present in the view.

Syntax

DBMS_DATA_MINING_TRANSFORM.XFORM_COL_REM (
     rem_table_name     IN       VARCHAR2,
     data_table_name    IN       VARCHAR2,
     xform_view_name    IN       VARCHAR2,
     rem_schema_name    IN       VARCHAR2 DEFAULT NULL,
     data_schema_name   IN       VARCHAR2 DEFAULT NULL,
     xform_schema_name  IN       VARCHAR2 DEFAULT NULL);

Parameters

Table 45-44 XFORM_COL_REM Procedure Parameters

Parameter Description

rem_table_name

Name of the transformation definition table for column removal. You can use the CREATE_COL_REM Procedure to create the definition table. See Table 45-10, "Columns in a Transformation Definition Table for Column Removal".

The table must be populated with column names before you call XFORM_COL_REM. The INSERT_BIN_SUPER Procedure and the INSERT_AUTOBIN_NUM_EQWIDTH Procedure can optionally be used to populate the table. You can also use SQL INSERT statements.

data_table_name

Name of the table containing the data to be transformed

xform_view_name

Name of the view to be created. The view presents the columns in data_table_name that are not specified in rem_table_name.

rem_schema_name

Schema of rem_table_name. If no schema is specified, the current schema is used.

data_schema_name

Schema of data_table_name. If no schema is specified, the current schema is used.

xform_schema_name

Schema of xform_view_name. If no schema is specified, the current schema is used.


Usage Notes

See "Operational Notes".

Examples

This example creates a view that includes all but one column from the table customers in the current schema.

describe customers
 Name                                      Null?    Type
 ----------------------------------------- -------- ----------------------------
 CUST_ID                                   NOT NULL NUMBER
 CUST_MARITAL_STATUS                                VARCHAR2(20)
 OCCUPATION                                         VARCHAR2(21)
 AGE                                                NUMBER
 YRS_RESIDENCE                                      NUMBER

BEGIN
    DBMS_DATA_MINING_TRANSFORM.CREATE_COL_REM ('colrem_xtbl');
END;
 /
INSERT INTO colrem_xtbl VALUES('CUST_MARITAL_STATUS', null);

BEGIN
   DBMS_DATA_MINING_TRANSFORM.XFORM_COL_REM (
     rem_table_name        => 'colrem_xtbl',
     data_table_name       => 'customers',
     xform_view_name       => 'colrem_view');
END;
/
describe colrem_view

 Name                                      Null?    Type
 ----------------------------------------- -------- ----------------------------
 CUST_ID                                   NOT NULL NUMBER
 OCCUPATION                                         VARCHAR2(21)
 AGE                                                NUMBER
 YRS_RESIDENCE                                      NUMBER

XFORM_EXPR_NUM Procedure

This procedure creates a view that implements the specified numeric transformations. Only the columns that you specify are transformed; the remaining columns from the data table are present in the view, but they are not changed.

Syntax

DBMS_DATA_MINING_TRANSFORM.XFORM_EXPR_NUM (
     expr_pattern       IN       VARCHAR2,
     data_table_name    IN       VARCHAR2,
     xform_view_name    IN       VARCHAR2,
     exclude_list       IN       COLUMN_LIST DEFAULT NULL,
     include_list       IN       COLUMN_LIST DEFAULT NULL,
     col_pattern        IN       VARCHAR2 DEFAULT ':col',
     data_schema_name   IN       VARCHAR2 DEFAULT NULL,
     xform_schema_name  IN       VARCHAR2 DEFAULT NULL);

Parameters

Table 45-45 XFORM_EXPR_NUM Procedure Parameters

Parameter Description

expr_pattern

A numeric transformation expression

data_table_name

Name of the table containing the data to be transformed

xform_view_name

Name of the view to be created. The view presents columns in data_table_name with the transformations specified in expr_pattern and col_pattern.

exclude_list

List of numeric columns to exclude. If NULL, no numeric columns are excluded.

The format of exclude_list is:

dbms_data_mining_transform.COLUMN_LIST('col1','col2',
                                       ...'coln')

include_list

List of numeric columns to include. If NULL, all numeric columns are included.

The format of include_list is:

dbms_data_mining_transform.COLUMN_LIST('col1','col2',
                                       ...'coln')

col_pattern

The value within expr_pattern that will be replaced with a column name. The value of col_pattern is case-sensitive.

The default value of col_pattern is ':col'

data_schema_name

Schema of data_table_name. If no schema is specified, the current schema is used.

xform_schema_name

Schema of xform_view_name. If no schema is specified, the current schema is used.


Usage Notes

  1. The XFORM_EXPR_NUM procedure constructs numeric transformation expressions from the specified expression pattern (expr_pattern) by replacing every occurrence of the specified column pattern (col_pattern) with an actual column name.

    XFORM_EXPR_NUM uses the SQL REPLACE function to construct the transformation expressions.

    REPLACE (expr_pattern,col_pattern,'"column_name"') || '"column_name"'
    

    If there is a column match, then the replacement is made in the transformation expression; if there is not a match, then the column is used without transformation.

    See:

    Oracle Database SQL Language Reference for information about the REPLACE function
  2. Because of the include and exclude list parameters, the XFORM_EXPR_NUM and XFORM_EXPR_STR procedures allow you to easily specify individual columns for transformation within large data sets. The other XFORM_* procedures support an exclude list only. In these procedures, you must enumerate every column that you do not want to transform.

  3. See "Operational Notes"

Examples

This example creates a view that transforms the data type of numeric columns.

describe customers
 Name                                Null?    Type
 ----------------------------------- -------- ------------------------
 CUST_ID                             NOT NULL NUMBER
 CUST_MARITAL_STATUS                          VARCHAR2(20)
 OCCUPATION                                   VARCHAR2(21)
 AGE                                          NUMBER
 YRS_RESIDENCE                                NUMBER

BEGIN
  DBMS_DATA_MINING_TRANSFORM.XFORM_EXPR_NUM(
    expr_pattern         => 'to_char(:col)',
    data_table_name      => 'customers',
    xform_view_name      => 'cust_nonum_view',
    exclude_list         => dbms_data_mining_transform.COLUMN_LIST( 'cust_id'),
    include_list         => null,
    col_pattern          => ':col');
END;
/
describe cust_nonum_view
 Name                                Null?    Type
 ----------------------------------- -------- ------------------------
 CUST_ID                             NOT NULL NUMBER
 CUST_MARITAL_STATUS                          VARCHAR2(20)
 OCCUPATION                                   VARCHAR2(21)
 AGE                                          VARCHAR2(40)
 YRS_RESIDENCE                                VARCHAR2(40)

XFORM_EXPR_STR Procedure

This procedure creates a view that implements the specified categorical transformations. Only the columns that you specify are transformed; the remaining columns from the data table are present in the view, but they are not changed.

Syntax

DBMS_DATA_MINING_TRANSFORM.XFORM_EXPR_STR (
     expr_pattern       IN       VARCHAR2,
     data_table_name    IN       VARCHAR2,
     xform_view_name    IN       VARCHAR2,
     exclude_list       IN       COLUMN_LIST DEFAULT NULL,
     include_list       IN       COLUMN_LIST DEFAULT NULL,
     col_pattern        IN       VARCHAR2 DEFAULT ':col',
     data_schema_name   IN       VARCHAR2 DEFAULT NULL,
     xform_schema_name  IN       VARCHAR2 DEFAULT NULL);

Parameters

Table 45-46 XFORM_EXPR_STR Procedure Parameters

Parameter Description

expr_pattern

A character transformation expression

data_table_name

Name of the table containing the data to be transformed

xform_view_name

Name of the view to be created. The view presents columns in data_table_name with the transformations specified in expr_pattern and col_pattern.

exclude_list

List of character columns to exclude. If NULL, no character columns are excluded.

The format of exclude_list is:

dbms_data_mining_transform.COLUMN_LIST('col1','col2',
                                       ...'coln')

include_list

List of character columns to include. If NULL, all character columns are included.

The format of include_list is:

dbms_data_mining_transform.COLUMN_LIST('col1','col2',
                                       ...'coln')

col_pattern

The value within expr_pattern that will be replaced with a column name. The value of col_pattern is case-sensitive.

The default value of col_pattern is ':col'

data_schema_name

Schema of data_table_name. If no schema is specified, the current schema is used.

xform_schema_name

Schema of xform_view_name. If no schema is specified, the current schema is used.


Usage Notes

  1. The XFORM_EXPR_STR procedure constructs character transformation expressions from the specified expression pattern (expr_pattern) by replacing every occurrence of the specified column pattern (col_pattern) with an actual column name.

    XFORM_EXPR_STR uses the SQL REPLACE function to construct the transformation expressions.

    REPLACE (expr_pattern,col_pattern,'"column_name"') || '"column_name"'
    

    If there is a column match, then the replacement is made in the transformation expression; if there is not a match, then the column is used without transformation.

    See:

    Oracle Database SQL Language Reference for information about the REPLACE function
  2. Because of the include and exclude list parameters, the XFORM_EXPR_STR and XFORM_EXPR_NUM procedures allow you to easily specify individual columns for transformation within large data sets. The other XFORM_* procedures support an exclude list only. In these procedures, you must enumerate every column that you do not want to transform.

  3. See "Operational Notes"

Examples

This example creates a view that transforms character columns to upper case.

describe customers
 Name                                Null?    Type
 ----------------------------------- -------- ------------------------
 CUST_ID                             NOT NULL NUMBER
 CUST_MARITAL_STATUS                          VARCHAR2(20)
 OCCUPATION                                   VARCHAR2(21)
 AGE                                          NUMBER
 YRS_RESIDENCE                                NUMBER

SELECT cust_id,  cust_marital_status, occupation FROM customers
    WHERE   cust_id > 102995
    ORDER BY cust_id desc;
 
CUST_ID CUST_MARITAL_STATUS  OCCUPATION
------- -------------------- ---------------------
 103000 Divorc.              Cleric.
 102999 Married              Cleric.
 102998 Married              Exec.
 102997 Married              Exec.
 102996 NeverM               Other
 
BEGIN
  DBMS_DATA_MINING_TRANSFORM.XFORM_EXPR_STR(
       expr_pattern            => 'upper(:col)',
       data_table_name         => 'customers',
       xform_view_name         => 'cust_upcase_view');
END;
/
describe cust_upcase_view
 Name                          Null?    Type
 ----------------------------- -------- --------------------
 CUST_ID                       NOT NULL NUMBER
 CUST_MARITAL_STATUS                    VARCHAR2(20)
 OCCUPATION                             VARCHAR2(21)
 AGE                                    NUMBER
 YRS_RESIDENCE                          NUMBER
 
SELECT cust_id,  cust_marital_status, occupation FROM cust_upcase_view
   WHERE   cust_id > 102995
   ORDER BY cust_id desc;
 
CUST_ID CUST_MARITAL_STATUS  OCCUPATION
------- -------------------- ---------------------
 103000 DIVORC.              CLERIC.
 102999 MARRIED              CLERIC.
 102998 MARRIED              EXEC.
 102997 MARRIED              EXEC.
 102996 NEVERM               OTHER

XFORM_MISS_CAT Procedure

This procedure creates a view that implements the categorical missing value treatment transformations specified in a definition table. Only the columns that are specified in the definition table are transformed; the remaining columns from the data table are present in the view, but they are not changed.

Syntax

DBMS_DATA_MINING_TRANSFORM.XFORM_MISS_CAT (
    miss_table_name       IN VARCHAR2, 
    data_table_name       IN VARCHAR2,
    xform_view_name       IN VARCHAR2,
    miss_schema_name      IN VARCHAR2 DEFAULT NULL,
    data_schema_name      IN VARCHAR2 DEFAULT NULL,
    xform_schema_name     IN VARCHAR2 DEFAULT NULL;

Parameters

Table 45-47 XFORM_MISS_CAT Procedure Parameters

Parameter Description

miss_table_name

Name of the transformation definition table for categorical missing value treatment. You can use the CREATE_MISS_CAT Procedure to create the definition table. The table must be populated with transformation definitions before you call XFORM_MISS_CAT. To populate the table, you can use the INSERT_MISS_CAT_MODE Procedure or you can write your own SQL.

See Table 45-12, "Columns in a Transformation Definition Table for Categorical Missing Value Treatment".

data_table_name

Name of the table containing the data to be transformed

xform_view_name

Name of the view to be created. The view presents columns in data_table_name with the transformations specified in miss_table_name.

miss_schema_name

Schema of miss_table_name. If no schema is specified, the current schema is used.

data_schema_name

Schema of data_table_name. If no schema is specified, the current schema is used.

xform_schema_name

Schema of xform_view_name. If no schema is specified, the current schema is used.


Usage Notes

See "Operational Notes".

Examples

This example creates a view that replaces missing categorical values with the mode.

SELECT * FROM geog;

REG_ID REGION
------ ------------------------------
     1 NE
     2 SW
     3 SE
     4 SW
     5
     6 NE
     7 NW
     8 NW
     9
    10
    11 SE
    12 SE
    13 NW
    14 SE
    15 SE
 
SELECT STATS_MODE(region) FROM geog;

STATS_MODE(REGION)
------------------------------
SE
 
BEGIN
  DBMS_DATA_MINING_TRANSFORM.CREATE_MISS_CAT('misscat_xtbl');
  DBMS_DATA_MINING_TRANSFORM.INSERT_MISS_CAT_MODE (
    miss_table_name         => 'misscat_xtbl',
    data_table_name         => 'geog' );
END;
/

SELECT col, val FROM misscat_xtbl;

COL        VAL
---------- ----------
REGION     SE
 
BEGIN
  DBMS_DATA_MINING_TRANSFORM.XFORM_MISS_CAT (
     miss_table_name        => 'misscat_xtbl',
     data_table_name        => 'geog',
     xform_view_name        => 'geogxf_view');
END;
/

SELECT * FROM geogxf_view;

REG_ID REGION
------ ------------------------------
     1 NE
     2 SW
     3 SE
     4 SW
     5 SE
     6 NE
     7 NW
     8 NW
     9 SE
    10 SE
    11 SE
    12 SE
    13 NW
    14 SE
    15 SE


XFORM_MISS_NUM Procedure

This procedure creates a view that implements the numerical missing value treatment transformations specified in a definition table. Only the columns that are specified in the definition table are transformed; the remaining columns from the data table are present in the view, but they are not changed.

Syntax

DBMS_DATA_MINING_TRANSFORM.XFORM_MISS_NUM (
    miss_table_name       IN VARCHAR2, 
    data_table_name       IN VARCHAR2,
    xform_view_name       IN VARCHAR2,
    miss_schema_name      IN VARCHAR2 DEFAULT NULL,
    data_schema_name      IN VARCHAR2 DEFAULT NULL,
    xform_schema_name     IN VARCHAR2 DEFAULT NULL;

Parameters

Table 45-48 XFORM_MISS_NUM Procedure Parameters

Parameter Description

miss_table_name

Name of the transformation definition table for numerical missing value treatment. You can use the CREATE_MISS_NUM Procedure to create the definition table. The table must be populated with transformation definitions before you call XFORM_MISS_NUM. To populate the table, you can use the INSERT_MISS_NUM_MEAN Procedure or you can write your own SQL.

See Table 45-14, "Columns in a Transformation Definition Table for Numerical Missing Value Treatment".

data_table_name

Name of the table containing the data to be transformed

xform_view_name

Name of the view to be created. The view presents columns in data_table_name with the transformations specified in miss_table_name.

miss_schema_name

Schema of miss_table_name. If no schema is specified, the current schema is used.

data_schema_name

Schema of data_table_name. If no schema is specified, the current schema is used.

xform_schema_name

Schema of xform_view_name. If no schema is specified, the current schema is used.


Usage Notes

See "Operational Notes".

Examples

This example creates a view that replaces missing numerical values with the mean.

SELECT * FROM items;
 
ITEM_ID       QTY
---------- ------
aa            200
bb            200
cc            250
dd
ee
ff            100
gg            250
hh            200
ii
jj            200
 
SELECT AVG(qty) FROM items;
 
AVG(QTY)
--------
     200
 
BEGIN
  DBMS_DATA_MINING_TRANSFORM.CREATE_MISS_NUM('missnum_xtbl');
  DBMS_DATA_MINING_TRANSFORM.INSERT_MISS_NUM_MEAN (
     miss_table_name          => 'missnum_xtbl',
     data_table_name         => 'items' );
END;
/
 
SELECT col, val FROM missnum_xtbl;
 
COL           VAL
---------- ------
QTY           200
 
BEGIN
    DBMS_DATA_MINING_TRANSFORM.XFORM_MISS_NUM (
        miss_table_name        => 'missnum_xtbl',
        data_table_name        => 'items',
        xform_view_name        => 'items_view');
END;
/
 
SELECT * FROM items_view;
 
ITEM_ID       QTY
---------- ------
aa            200
bb            200
cc            250
dd            200
ee            200
ff            100
gg            250
hh            200
ii            200
jj            200

XFORM_NORM_LIN Procedure

This procedure creates a view that implements the linear normalization transformations specified in a definition table. Only the columns that are specified in the definition table are transformed; the remaining columns from the data table are present in the view, but they are not changed.

Syntax

DBMS_DATA_MINING_TRANSFORM.XFORM_NORM_LIN (
     norm_table_name      IN VARCHAR2,
     data_table_name      IN VARCHAR2,
     xform_view_name      IN VARCHAR2,
     norm_schema_name     IN VARCHAR2 DEFAULT NULL,
     data_schema_name     IN VARCHAR2 DEFAULT NULL,
     xform_schema_name    IN VARCHAR2 DEFAULT NULL);

Parameters

Table 45-49 XFORM_NORM_LIN Procedure Parameters

Parameter Description

norm_table_name

Name of the transformation definition table for linear normalization. You can use the CREATE_NORM_LIN Procedure to create the definition table. The table must be populated with transformation definitions before you call XFORM_NORM_LIN. To populate the table, you can use one of the INSERT procedures for normalization or you can write your own SQL.

See Table 45-12, "Columns in a Transformation Definition Table for Categorical Missing Value Treatment".

data_table_name

Name of the table containing the data to be transformed

xform_view_name

Name of the view to be created. The view presents columns in data_table_name with the transformations specified in miss_table_name.

norm_schema_name

Schema of miss_table_name. If no schema is specified, the current schema is used.

data_schema_name

Schema of data_table_name. If no schema is specified, the current schema is used.

xform_schema_name

Schema of xform_view_name. If no schema is specified, the current schema is used.


Usage Notes

See "Operational Notes".

Examples

This example creates a view that normalizes the cust_year_of_birth and cust_credit_limit columns. The data source consists of three columns from sh.customer.

CREATE OR REPLACE VIEW mining_data AS
     SELECT cust_id, cust_year_of_birth, cust_credit_limit
     FROM sh.customers;

describe mining_data
 Name                                   Null?    Type
 -------------------------------------- -------- --------------------------
 CUST_ID                                NOT NULL NUMBER
 CUST_YEAR_OF_BIRTH                     NOT NULL NUMBER(4)
 CUST_CREDIT_LIMIT                               NUMBER
 
SELECT * FROM mining_data WHERE cust_id > 104495
        ORDER BY cust_year_of_birth;
 
 CUST_ID CUST_YEAR_OF_BIRTH CUST_CREDIT_LIMIT
-------- ------------------ -----------------
  104496               1947              3000
  104498               1954             10000
  104500               1962             15000
  104499               1970              3000
  104497               1976              3000
 
BEGIN
  dbms_data_mining_transform.CREATE_NORM_LIN(
       norm_table_name            => 'normx_tbl');
 dbms_data_mining_transform.INSERT_NORM_LIN_MINMAX(
      norm_table_name      => 'normx_tbl',
      data_table_name      => 'mining_data',
      exclude_list        => dbms_data_mining_transform.COLUMN_LIST( 'cust_id'),
      round_num   => 3);
END;
/
 
SELECT col, shift, scale FROM normx_tbl;
 
COL                               SHIFT    SCALE                           
------------------------------ -------- --------                           
CUST_YEAR_OF_BIRTH                 1910       77                           
CUST_CREDIT_LIMIT                  1500    13500                           
 
BEGIN
  DBMS_DATA_MINING_TRANSFORM.XFORM_NORM_LIN (
     norm_table_name       => 'normx_tbl',
     data_table_name       => 'mining_data',
     xform_view_name       => 'norm_view');
END;
/
 
SELECT * FROM norm_view WHERE cust_id > 104495
        ORDER BY cust_year_of_birth;
 
 CUST_ID CUST_YEAR_OF_BIRTH CUST_CREDIT_LIMIT
-------- ------------------ -----------------
  104496           .4805195          .1111111
  104498           .5714286          .6296296
  104500           .6753247                 1
  104499           .7792208          .1111111
  104497           .8571429          .1111111
 
 
set long 2000
SQL> SELECT text FROM user_views WHERE view_name IN 'NORM_VIEW';
 
TEXT                                                                       
---------------------------------------------------------------------------
SELECT "CUST_ID",("CUST_YEAR_OF_BIRTH"-1910)/77 "CUST_YEAR_OF_BIRTH",("CUST
_CREDIT_LIMIT"-1500)/13500 "CUST_CREDIT_LIMIT" FROM mining_data            
                                                                           

XFORM_STACK Procedure

This procedure creates a view that implements the transformations specified by the stack. Only the columns and nested attributes that are specified in the stack are transformed. Any remaining columns and nested attributes from the data table appear in the view without changes.

To create a list of objects that describe the transformed columns, use the DESCRIBE_STACK Procedure.

See Also:

"Overview"

Oracle Data Mining Application Developer's Guide for more information about data mining attributes

Syntax

DBMS_DATA_MINING_TRANSFORM.XFORM_STACK (
     xform_list         IN     TRANSFORM_list,
     data_table_name    IN     VARCHAR2,
     xform_view_name    IN     VARCHAR2,
     data_schema_name   IN     VARCHAR2 DEFAULT NULL,
     xform_schema_name  IN     VARCHAR2 DEFAULT NULL);

Parameters

Table 45-50 XFORM_STACK Procedure Parameters

Parameter Description

xform_list

The transformation list. See Table 45-1 for a description of the TRANSFORM_LIST object type.

data_table_name

Name of the table containing the data to be transformed

xform_view_name

Name of the view to be created. The view applies the transformations in xform_list to data_table_name.

data_schema_name

Schema of data_table_name. If no schema is specified, the current schema is used.

xform_schema_name

Schema of xform_view_name. If no schema is specified, the current schema is used.


Usage Notes

See "Operational Notes". The following sections are especially relevant:

Examples

This example applies a transformation list to the view dmuser.cust_info and shows how the data is transformed.The CREATE statement for cust_info is shown in "DESCRIBE_STACK Procedure".

BEGIN
  dbms_data_mining_transform.CREATE_BIN_NUM ('birth_yr_bins');
  dbms_data_mining_transform.INSERT_BIN_NUM_QTILE (
        bin_table_name   => 'birth_yr_bins',
        data_table_name  =>  'cust_info',
        bin_num          =>   6,
        exclude_list     =>  dbms_data_mining_transform.column_list(
                                 'cust_id','country_id'));
END;
/
SELECT * FROM birth_yr_bins;

COL                  ATT      VAL BIN
-------------------- ----- ------ ----------
CUST_YEAR_OF_BIRTH           1922
CUST_YEAR_OF_BIRTH           1951 1
CUST_YEAR_OF_BIRTH           1959 2
CUST_YEAR_OF_BIRTH           1966 3
CUST_YEAR_OF_BIRTH           1973 4
CUST_YEAR_OF_BIRTH           1979 5
CUST_YEAR_OF_BIRTH           1986 6

DECLARE
      cust_stack   dbms_data_mining_transform.TRANSFORM_LIST;
BEGIN
      dbms_data_mining_transform.SET_TRANSFORM (cust_stack,
          'country_id', NULL, 'country_id/10', 'country_id*10');
      dbms_data_mining_transform.STACK_BIN_NUM ('birth_yr_bins',
          cust_stack);
      dbms_data_mining_transform.SET_TRANSFORM (cust_stack,
          'custprods', 'Mouse Pad', 'value*100', 'value/100');
      dbms_data_mining_transform.XFORM_STACK(
          xform_list        => cust_stack,
          data_table_name   => 'cust_info',
          xform_view_name   =>  'cust_xform_view');
  END;
/

-- Two rows of data without transformations
SELECT * from cust_info WHERE cust_id BETWEEN 100010 AND 100011;

CUST_ID COUNTRY_ID CUST_YEAR_OF_BIRTH CUSTPRODS(ATTRIBUTE_NAME, VALUE)
------- ---------- ------------------ -----------------------------------------
 100010      52790               1975 DM_NESTED_NUMERICALS(
                                        DM_NESTED_NUMERICAL(
                                         '18" Flat Panel Graphics Monitor', 1),
                                        DM_NESTED_NUMERICAL(
                                         'SIMM- 16MB PCMCIAII card', 1))
 100011      52775               1972 DM_NESTED_NUMERICALS(
                                       DM_NESTED_NUMERICAL(
                                         'External 8X CD-ROM', 1),
                                       DM_NESTED_NUMERICAL(
                                         'Mouse Pad', 1),
                                       DM_NESTED_NUMERICAL(
                                        'SIMM- 16MB PCMCIAII card', 1),
                                       DM_NESTED_NUMERICAL(
                                         'Keyboard Wrist Rest', 1),
                                       DM_NESTED_NUMERICAL(
                                         '18" Flat Panel Graphics Monitor', 1),
                                       DM_NESTED_NUMERICAL(
                                         'O/S Documentation Set - English', 1))

-- Same two rows of data with transformations
SELECT * FROM cust_xform_view WHERE cust_id BETWEEN 100010 AND 100011;
 
CUST_ID  COUNTRY_ID  C  CUSTPRODS(ATTRIBUTE_NAME, VALUE)
-------  ----------  -  --------------------------------------------------------
 100010       5279   5  DM_NESTED_NUMERICALS(
                          DM_NESTED_NUMERICAL(
                           '18" Flat Panel Graphics Monitor', 1),
                          DM_NESTED_NUMERICAL(
                            'SIMM- 16MB PCMCIAII card', 1))
 100011     5277.5   4  DM_NESTED_NUMERICALS(
                          DM_NESTED_NUMERICAL(
                            'External 8X CD-ROM', 1),
                          DM_NESTED_NUMERICAL(
                            'Mouse Pad', 100),
                          DM_NESTED_NUMERICAL(
                            'SIMM- 16MB PCMCIAII card', 1),
                          DM_NESTED_NUMERICAL(
                            'Keyboard Wrist Rest', 1),
                          DM_NESTED_NUMERICAL(
                            '18" Flat Panel Graphics Monitor', 1),
                          DM_NESTED_NUMERICAL(
                            'O/S Documentation Set - English', 1))