25 DBMS_DATA_MINING

Using DBMS_DATA_MINING

This section contains topics which relate to using the DBMS_DATA_MINING package.

• Overview

• New Functionality

• Model Names

• Constants

• Data Types

• Exceptions

• User Views

Overview

Oracle Data Mining (ODM) embeds data mining functionality in the Oracle Database. The data never leaves the database — the data, its preparation, model building, and model scoring (applying) all remain in the database. This enables Oracle to provide an infrastructure for data analysts and application developers to integrate data mining seamlessly with database applications.

Oracle Data Mining Concepts

ODM supports both predictive and descriptive data mining. Predictive data mining uses an historical model to predict a target value. Descriptive data mining identifies natural groupings within a given data set.

Predictive data mining functions include:

• Classification

• Regression

• Attribute Importance

Descriptive data mining functions include:

• Clustering

• Association

• Feature Extraction

The steps you use to build and score a model depend on the data mining function and the algorithm being used. To learn more about ODM functions and algorithms, refer to Oracle Data Mining Concepts.

Oracle Data Mining APIs

ODM provides a graphical user interface (Oracle Data Miner), as well as application programming interfaces for SQL and Java. The SQL interface consists of PL/SQL packages and SQL functions. The Java interface is an Oracle implementation of the JDM 1.0 standard for data mining. The SQL and Java APIs are fully interoperable.

The SQL functions for data mining, new in 10g Release 2 (10.2), return the results of model scoring. These functions apply pre-existing models within the context of a SQL statement. The ODM scoring functions include: CLUSTER_ID, CLUSTER_PROBABILITY, CLUSTER_SET, FEATURE_ID, FEATURE_SET, FEATURE_VALUE, PREDICTION, PREDICTION_COST, PREDICTION_DETAILS, PREDICTION_PROBABILITY, PREDICTION_SET. The ODM scoring functions are documented in Oracle Database SQL Reference.

The DBMS_DATA_MINING package supports the process of building, testing, and scoring models for all ODM mining functions. Most mining data requires preprocessing before mining activities can begin. For this, you can use the DBMS_DATA_MINING_TRANSFORM package or third-party utilities. To automate the entire process of predictive data mining, use the DBMS_PREDICTIVE_ANALYTICS package.

New Functionality

In Oracle Database 10g Release 2, the DBMS_DATA_MINING package includes the following new functionality:

• Decision Tree algorithm for classification

• Orthogonal Partitioning Clustering (O-Cluster) algorithm for clustering

• One-Class Support Vector Machine, which supports Anomaly Detection

• Active learning for Support Vector Machine

For detailed information about new features in Oracle Data Mining, see Oracle Data Mining Concepts and Oracle Database New Features

Model Names

The names of ODM models must be valid schema object names. However, the naming rules for models are more restrictive than the naming rules for schema objects. A model name must satisfy the following additional requirements:

• It must be 25 or fewer characters long.

• It must be a nonquoted identifier. Oracle requires that nonquoted identifiers contain only alphanumeric characters, the underscore (_), dollar sign ($), and pound sign (#); the initial character must be alphabetic. Oracle strongly discourages the use of the dollar sign and pound sign in nonquoted literals.

Naming requirements for schema objects are fully documented in Oracle Database SQL Reference.

Constants

Oracle Data Mining uses constants to specify the mining function, its algorithm, and other details about a model. The function, or type, of a model is specified when the model is created. Non-default characteristics of the model are specified in a settings table associated with the model.

The settings table is a user-created table with the following columns:

(setting_name        VARCHAR2(30),
 setting_value       VARCHAR2(128))

Each setting in the setting_name column is a constant, and many of the values that can be specified in the setting_value column are also constants. Numeric values are implicitly converted to VARCHAR2. To explicitly convert them, use the TOCHAR function.

Constants that Specify the Mining Function

Oracle Data Mining supports a number of predictive and descriptive mining functions. The mining function is specified as a parameter to the CREATE_MODEL procedure. See "CREATE_MODEL Procedure" for more information.

The mining_function parameter has a VARCHAR2(30) data type; it can have the values listed in Table 25-1.

Constants that Specify Information About Mining Functions

Every model is based on one of the mining functions described in Table 25-1. You can configure the mining function with the settings described in Table 25-2.

Constants that Specify Information About Algorithms

The algorithm used by a model is specified by the algo_name setting (described in Table 25-2). You can configure the algorithm with the settings described in Table 25-3.

Data Types

The DBMS_DATA_MINING package includes a number of table functions that return algorithm-specific information about models. These functions take a model name as input and return the requested information as a collection of rows. The table functions are named GET_n, where n identifies the type of information to return. For a list of the ODM GET functions, see "Summary of DBMS_DATA_MINING Subprograms".

All the GET functions use pipelining, which causes each row of output to be materialized as it is read from model storage, without waiting for the generation of the complete table object. For more information on pipelined, parallel table functions, consult the Oracle Database PL/SQL User's Guide and Reference.

The virtual table returned by the GET functions is an object data type. Another object data type defines the rows. Some of the columns have object data types that define nested tables.

ODM also uses object data types for handling wide data. These types, DM_NESTED_NUMERICALS and DM_NESTED_CATEGORICALS define nested tables that can be used for storing a set of mining attributes in a single column. For more information on wide data, see the Oracle Data Mining Application Developer's Guide.

The ODM object data types are described in Table 25-4.

Exceptions

The following table lists the exceptions raised by DBMS_DATA_MINING.

User Views

Table 25-6 describes the DM_USER_MODELS view, which provides information about the models in the user's schema.

Summary of DBMS_DATA_MINING Subprograms

Table 25-7 summarizes the subprograms included in the DBMS_DATA_MINING package.

APPLY Procedure

This procedure applies a mining model to the data of interest, and generates the APPLY results in a table. The APPLY operation is also referred to as scoring.

For predictive mining functions, the APPLY operation generates predictions in a target column. For descriptive mining functions such as clustering, the APPLY operation assigns each case to a cluster with a probability.

The APPLY operation is not applicable to association models and attribute importance models.

Syntax

DBMS_DATA_MINING.APPLY (
      model_name           IN VARCHAR2,
      data_table_name      IN VARCHAR2,
      case_id_column_name  IN VARCHAR2,
      result_table_name    IN VARCHAR2,
      data_schema_name     IN VARCHAR2 DEFAULT NULL);

Parameters

Usage Notes

The data provided for APPLY should match the data provided to CREATE_MODEL in terms of the schema definition and relevant content. The GET_MODEL_SIGNATURE function provides this information. If the data provided as input to CREATE_MODEL has been pre-processed, then the data input to APPLY must be pre-processed in the same way. The case identifier is not considered to be a mining attribute during APPLY.

You must provide the name of the table in which the results of the apply operation are to be stored. APPLY creates a table with an algorithm-specific fixed schema in the user schema that owns the model.

The behavior of an APPLY operation is analogous to a SQL query operation, even though it is packaged as a procedure. It does not update the model contents and does not have any contention with CREATE_MODEL, DROP_MODEL, or RENAME_MODEL operations. The corollary is that if you potentially drop or rename a model while a model is being applied to scoring data, the APPLY operation may discontinue with partial or unpredictable results.

The schema for the apply results from each of the supported algorithms is listed in subsequent sections. The case_id column will match the case identifier column name provided by you. The type of incoming case_id column is preserved in APPLY output.

Classification Algorithms

The table containing the APPLY results for all classification models has the same definition. For numerical targets, the results table will have the following columns.

case_id      VARCHAR2/NUMBER 
prediction   NUMBER
probability  NUMBER

For categorical targets, the results table will have the following columns.

case_id      VARCHAR2/NUMBER 
prediction   VARCHAR2
probability  NUMBER

One-Class SVM (Anomaly Detection)

The results table will have the following columns.

case_id       VARCHAR2/NUMBER 
prediction    NUMBER
probability   NUMBER

Values in the prediction column can be either 0 or 1. When the prediction is 1, the case is a typical example. When the prediction is 0, the case is an outlier.

Regression using SVM

The results table will have the following columns.

case_id     VARCHAR2/NUMBER 
prediction  NUMBER

Clustering using k-Means and O-Cluster

Clustering is an unsupervised mining function, and hence there are no targets. The results of an APPLY operation will contain simply the cluster identifier corresponding to a case, and the associated probability. The results table will have the following columns.

case_id      VARCHAR2/NUMBER 
cluster_id   NUMBER
probability  NUMBER

Feature Extraction using NMF

Feature extraction is also an unsupervised mining function, and hence there are no targets. The results of an APPLY operation will contain simply the feature identifier corresponding to a case, and the associated match quality. The results table will have the following columns

case_id        VARCHAR2/NUMBER 
feature_id     NUMBER
match_quality  NUMBER

Examples

BEGIN
/* build a model with name census_model.
 * (See example under CREATE_MODEL)
 */ 

/* if build data was pre-processed in any manner,
 * perform the same pre-processing steps on the
 * scoring data also.
 * (See examples in the section on DBMS_DATA_MINING_TRANSFORM)
 */

/* apply the model to data to be scored */
DBMS_DATA_MINING.APPLY(
  model_name          => 'census_model',
  data_table_name     => 'census_2d_apply',
  case_id_column_name => 'person_id',
  result_table_name   => 'census_apply_result');
END;
/

-- View Apply Results
SELECT case_id, prediction, probability
  FROM census_apply_result;

COMPUTE_CONFUSION_MATRIX Procedure

This procedure computes the confusion matrix for a classification model and also provides the accuracy of the model. See Oracle Data Mining Concepts for a description of confusion matrix.

Before executing a COMPUTE_CONFUSION_MATRIX procedure:

• Apply the model on the test data

• Create a target table or view containing only the case identifier and target columns from the test data

You will specify this table or view and the apply results table as input to the procedure.

Syntax

DBMS_DATA_MINING.COMPUTE_CONFUSION_MATRIX (
      accuracy                     OUT NUMBER,
      apply_result_table_name      IN  VARCHAR2,
      target_table_name            IN  VARCHAR2,
      case_id_column_name          IN  VARCHAR2,
      target_column_name           IN  VARCHAR2,
      confusion_matrix_table_name  IN  VARCHAR2,
      score_column_name            IN  VARCHAR2 DEFAULT 'PREDICTION',
      score_criterion_column_name  IN  VARCHAR2 DEFAULT 'PROBABILITY',
      cost_matrix_table_name       IN  VARCHAR2 DEFAULT NULL,
      apply_result_schema_name     IN  VARCHAR2 DEFAULT NULL,
      target_schema_name           IN  VARCHAR2 DEFAULT NULL,
      cost_matrix_schema_name      IN  VARCHAR2 DEFAULT NULL);

Parameters

Usage Notes

You can also provide a cost matrix as an optional input in order to have the cost of predictions reflected in the results.

It is important to note that the inputs to COMPUTE_CONFUSION_MATRIX do not always have to be generated using APPLY. As long as the definition of the two input tables matches the ones discussed in this section, with appropriate content, the procedure can produce the confusion matrix and accuracy. The quality of the results depends on the quality of the data.

The data provided for testing your classification model must match the data provided to CREATE_MODEL in schema and relevant content. If the data provided as input to CREATE_MODEL has been pre-processed, then the data input to APPLY must also be pre-processed using the statistics from the CREATE_MODEL data pre-processing.

Before you use the COMPUTE_CONFUSION_MATRIX procedure, you must prepare two data input streams from your test data.

First, you must APPLY the model on your test data. Use the result table name from APPLY as apply_result_table_name in the COMPUTE_CONFUSION_MATRIX procedure.

Next, you must create a table or view containing only the case identifier column and the target column in its schema. Use the name of this second table as target_table_name.

The definition for the second view or table name for a numerical target attribute is:

(case_identifier_column_name  VARCHAR2/NUMBER, 
target_column_name            NUMBER)

The definition for the second view or table name for a categorical target attribute is:

(case_identifier_column_name  VARCHAR2/NUMBER, 
target_column_name            NUMBER)

You must provide the name of the table in which the confusion matrix is to be generated. The resulting fixed schema table will always be created in the schema owning the model.

For numerical target attributes, the confusion matrix table will have the definition:

(actual_target_value    NUMBER,
predicted_target_value  NUMBER,
value                   NUMBER)

For categorical target attributes, the confusion matrix table will have the definition:

(actual_target_value     VARCHAR2,
predicted_target_value   VARCHAR2,
value                    NUMBER)

Examples

Assume that you have built a classification model census_model using the Naive Bayes algorithm, and you have been provided the test data in a table called census_2d_test, with case identifier column name person_id, and the target column name class.

DECLARE
  v_sql_stmt VARCHAR2(4000);
  v_accuracy NUMBER;
BEGIN

/* apply the model census_model on test data */
DBMS_DATA_MINING.APPLY(
  model_name           => 'census_model',
  data_table_name      => 'census_2d_test',
  case_id_column_name  => 'person_id',
  result_table_name    => 'census_test_result');
CREATE VIEW census_2d_test_view as select person_id, class from census_2d_test;

/* now compute the confusion matrix from the two
 * data streams, also providing a cost matrix as input.
 */
DBMS_DATA_MINING.COMPUTE_CONFUSION_MATRIX (
  accuracy                     => v_accuracy,
  apply_result_table_name      => 'census_test_result',
  target_table_name            => 'census_2d_test_view',
  case_id_column_name          => 'person_id',
  target_column_name           => 'class',
  confusion_matrix_table_name  => 'census_confusion_matrix',
  cost_matrix_table_name       => 'census_cost_matrix');
DBMS_OUTPUT.PUT_LINE('Accuracy of the model: ' || v_accuracy);
END;
/

-- View the confusion matrix using Oracle SQL
SELECT actual_target_value, predicted_target_value, value
  FROM census_confusion_matrix;

COMPUTE_LIFT Procedure

This procedure computes a lift table for a given positive target for a classification model. See Oracle Data Mining Concepts for a description of lift.

Before executing a COMPUTE_LIFT procedure:

• Apply the model on the test data

• Create a target table or view containing only the case identifier and target columns from the test data

You will specify this table or view and the apply results table as input to the procedure.

Syntax

DBMS_DATA_MINING.COMPUTE_LIFT (
      apply_result_table_name      IN VARCHAR2,
      target_table_name            IN VARCHAR2,
      case_id_column_name          IN VARCHAR2,
      target_column_name           IN VARCHAR2,
      lift_table_name              IN VARCHAR2,
      positive_target_value        IN VARCHAR2,
      score_column_name            IN VARCHAR2 DEFAULT 'PREDICTION',
      score_criterion_column_name  IN VARCHAR2 DEFAULT 'PROBABILITY',
      num_quantiles                IN NUMBER DEFAULT 10,
      cost_matrix_table_name       IN VARCHAR2 DEFAULT NULL,
      apply_result_schema_name     IN VARCHAR2 DEFAULT NULL,
      target_schema_name           IN VARCHAR2 DEFAULT NULL,
      cost_matrix_schema_name      IN VARCHAR2 DEFAULT NULL);

Parameters

Usage Notes

You can also provide a cost matrix as an optional input to have the cost of predictions reflected in the results.

It is important to note that the data inputs to COMPUTE_LIFT do not always have to be generated using APPLY. As long as the schema of the two input tables matches the ones discussed in this section, with appropriate content, the procedure can provide the lift table as output. The quality of the results depends on the quality of the data.

The data provided for testing your classification model must match the data provided to CREATE_MODEL in schema and relevant content. If the data provided as input to CREATE_MODEL has been pre-processed, then the data input to APPLY must also be pre-processed using the same binning table used in build pre-processing.

Before you use the COMPUTE_LIFT procedure, you must prepare two data input streams from your test data.

First, you must APPLY the model on your test data. The parameter apply_result_table_name in the COMPUTE_LIFT procedure represents the table that will be generated in your schema as a result of the APPLY operation.

Next, you must create a table or view containing only the case identifier column and the target column in its schema. The parameter target_table_name reflects this input. The definition for this view or table name for a numerical target attribute is:

(case_identifier_column_name  VARCHAR2/NUMBER,
target_column_name             NUMBER)

The definition for this view or table name for a categorical target attribute is:

(case_identifier_column_name  VARCHAR2/NUMBER,
target_column_name            NUMBER)

You must provide the name of the table in which the lift table is to be generated. The resulting fixed schema table is always created in the schema that owns the model.

The resulting lift table will have the following definition:

(quantile_number               NUMBER,
 probability_threshold         NUMBER,
 gain_cumulative               NUMBER,
 quantile_total_count          NUMBER,
 quantile_target_count         NUMBER,
 percent_records_cumulative    NUMBER,
 lift_cumulative               NUMBER,
 target_density_cumulative     NUMBER,
 targets_cumulative            NUMBER,
 non_targets_cumulative        NUMBER,
 lift_quantile                 NUMBER,
 target_density                NUMBER)

When a cost matrix is passed to the COMPUTE_LIFT procedure, the cost threshold is returned in the probability_threshold column.

The output columns are explained in Oracle Data Mining Concepts.

Examples

Assume that you have built a classification model census_model using the Naive Bayes algorithm, and you have been provided the test data in a table called census_2d_test, with case identifier column name person_id, and the target column name class.

DECLARE
  v_sql_stmt VARCHAR2(4000);
BEGIN

/* apply the model census_model on test data */
DBMS_DATA_MINING.APPLY(
  model_name           => 'census_model',
  data_table_name      => 'census_2d_test,
  case_id_column_name  => 'person_id',
  result_table_name    => 'census_test_result');

/* next create a view from test data that projects
 * only the case identifier and target column
 */

/* now compute lift with the default 10 quantiles
 * from the two data streams
 */
DBMS_DATA_MINING.COMPUTE_LIFT (
  apply_result_table_name   => 'census_test_result',
  target_table_name         => 'census_2d_test_view',
  case_id_column_name       => 'person_id',
  target_column_name        => 'class',
  lift_table_name           => 'census_lift',
  positive_target_value     => '1',
  cost_matrix_table_name    => 'census_cost_matrix');
END;
/

-- View the lift table contents using SQL
SELECT *
  FROM census_lift;

COMPUTE_ROC Procedure

This procedure computes the receiver operating characteristic (ROC) for a binary classification model. See Oracle Data Mining Concepts for a description of receiver operating characteristic.

Before executing a COMPUTE_ROC procedure:

• Apply the model on the test data

• Create a target table or view containing only the case identifier and target columns from the test data

You will specify this table or view and the apply results table as input to the procedure.

Syntax

DBMS_DATA_MINING.COMPUTE_ROC (
      roc_area_under_curve         OUT NUMBER,
      apply_result_table_name      IN  VARCHAR2,
      target_table_name            IN  VARCHAR2,
      case_id_column_name          IN  VARCHAR2,
      target_column_name           IN  VARCHAR2,
      roc_table_name               IN  VARCHAR2,
      positive_target_value        IN  VARCHAR2,
      score_column_name            IN  VARCHAR2 DEFAULT 'PREDICTION',
      score_criterion_column_name  IN  VARCHAR2 DEFAULT 'PROBABILITY',
      apply_result_schema_name     IN  VARCHAR2 DEFAULT NULL,
      target_schema_name           IN  VARCHAR2 DEFAULT NULL);

Parameters

Usage Notes

It is important to note that the data inputs to COMPUTE_ROC do not always have to be generated using APPLY. As long as the schema of the two input tables matches the ones discussed in this section, with appropriate content, the procedure can provide the ROC table as output. The quality of the results depends on the quality of the data.

The data provided for testing your classification model must match the data provided to CREATE_MODEL in schema and relevant content. If the data provided as input to CREATE_MODEL has been pre-processed, then the data input to APPLY must also be pre-processed using the statistics from the CREATE_MODEL data pre-processing.

Before you use the COMPUTE_ROC procedure, you must prepare two data input streams from your test data.

First, you must APPLY the model on your test data. The parameter apply_result_table_name in the COMPUTE_ROC procedure identifies the table that will be generated in your schema as a result of the APPLY operation.

Next, you must create a table or view containing only the case identifiers and target values from the test data. The parameter target_table_name identifies this table. For a numerical target attribute, the columns of this table are:

case_identifier_column_name  VARCHAR2/NUMBER,
target_column_name           NUMBER

For a categorical target attribute, the columns of this table are:

case_identifier_column_name  VARCHAR2/NUMBER,
target_column_name           VARCHAR2

You must provide the name of the table in which the ROC table is to be generated. The resulting table will always be created in the schema that owns the model, and it will always have the following columns.

(probability              NUMBER,
 true_positives           NUMBER,
 false_negatives          NUMBER,
 false_positives          NUMBER,
 true_negatives           NUMBER,
 true_positive_fraction   NUMBER,
 false_positive_fraction  NUMBER)

The output columns are explained in Table 25-12.

The typical use scenario is to examine the true_positive_fraction and false_positive_fraction to determine the most desirable probability_threshold. This threshold is then used to predict class values in subsequent apply operations. For example, to identify positively predicted cases in probability rank order from an apply result table, given a probability_threshold:

select case_id_column_name from apply_result_table_name where probability > probability_threshold order by probability DESC;

There are two procedures one might use to identify the most desirable probability_threshold. One procedure applies when the relative cost of positive class versus negative class prediction errors are known to the user. The other applies when such costs are not well known to the user. In the first instance, one can apply the relative costs to the ROC table to compute the minimum cost probability_threshold. Suppose the relative cost ratio, Positive Class Error Cost / Negative Class Error Cost = 20. Then execute a query like:

WITH cost AS (
  SELECT probability_threshold, 20 * false_negatives + false positives cost 
    FROM ROC_table 
  GROUP BY probability_threshold), 
    minCost AS (
      SELECT min(cost) minCost 
        FROM cost)
      SELECT max(probability_threshold)probability_threshold 
        FROM cost, minCost 
    WHERE cost = minCost;

If relative costs are not well known, the user simply scans the values in the table (in sorted order) and makes a determination about which of the displayed trade-offs (misclassified positives versus misclassified negatives) is most desirable:

select * from ROC_table order by probability_threshold

Examples

Assume that you have built a classification model census_model using the SVM algorithm, and you have been provided the test data in a table called census_2d_test, with case identifier column name person_id, and the target column name class.

DECLARE
  v_sql_stmt VARCHAR2(4000);
  v_accuracy NUMBER;
BEGIN

/* apply the model census_model on test data */
DBMS_DATA_MINING.APPLY(
  model_name           => 'census_model',
  data_table_name      => 'census_2d_test',
  case_id_column_name  => 'person_id',
  result_table_name    => 'census_test_result');

/* next create a view from test data that projects
 * only the case identifier and target column
 */
v_sql_stmt :=
'CREATE VIEW census_2d_test_view AS ' ||
'SELECT person_id, class FROM census_2d_test';
EXECUTE IMMEDIATE v_sql_stmt;

/* now compute the receiver operating characterestics from
 * the two data streams, also providing a cost matrix
 * as input.
 */
DBMS_DATA_MINING.COMPUTE_ROC (
  accuracy                 => v_accuracy,
  apply_result_table_name  => 'census_test_result',
  target_table_name        => 'census_2d_test_view',
  case_id_column_name      => 'person_id',
  target_column_name       => 'class',
  roc_table_name           => 'census_roc',
  cost_matrix_table_name   => 'census_cost_matrix');
END;
/

-- View the ROC results using Oracle SQL
SELECT *
  FROM census_roc;

CREATE_MODEL Procedure

This procedure creates a mining model for a given mining function

Syntax

DBMS_DATA_MINING.CREATE_MODEL (
      model_name            IN VARCHAR2,
      mining_function       IN VARCHAR2,
      data_table_name       IN VARCHAR2,
      case_id_column_name   IN VARCHAR2,
      target_column_name    IN VARCHAR2 DEFAULT NULL,
      settings_table_name   IN VARCHAR2 DEFAULT NULL,
      data_schema_name      IN VARCHAR2 DEFAULT NULL,
      settings_schema_name  IN VARCHAR2 DEFAULT NULL);

Parameters

Usage Notes

The data provided to all subsequent operations such as APPLY must match the data provided to CREATE_MODEL in schema and relevant content. If the data provided as input to CREATE_MODEL has been pre-processed, then the data input to subsequent operations such as APPLY must also be pre-processed using the statistics from the CREATE_MODEL data pre-processing. The case identifier column is not considered to be a mining attribute during CREATE_MODEL.

You can view the default settings for each algorithm through GET_DEFAULT_SETTINGS. You can override the defaults by providing a settings table specifying your choice of mining algorithm and relevant overriding algorithm settings.

Once a model has been built, information about the attributes used for model build can be obtained from GET_MODEL_SIGNATURE. To inspect or review model contents, you can use any of the algorithm-specific GET_MODEL_DETAILS functions.

The behavior of the CREATE_MODEL is analogous to a SQL DDL CREATE operation. It contends with RENAME_MODEL and DROP_MODEL operations.

Examples

The first example builds a classification model using the Support Vector Machine algorithm.

/* prepare a settings table to override default
 * settings (Naive Bayes is the default classifier)
 */
CREATE TABLE census_settings (
  setting_name  VARCHAR2(30),
  setting_value VARCHAR2(128));

BEGIN
/* indicate that SVM is the chosen classifier */
INSERT INTO census_settings VALUES (
DBMS_DATA_MINING.ALGO_NAME, DBMS_DATA_MINING.ALGO_SUPPORT_VECTOR_MACHINES);

/* override the default value for complexity factor */
INSERT INTO census_settings (setting_name, setting_value)
VALUES (dbms_data_mining.svms_complexity_factor, TO_CHAR(0.081));
COMMIT;

/* build a model with name census_model */
DBMS_DATA_MINING.CREATE_MODEL(
  model_name           => 'census_model',
  mining_function      => DBMS_DATA_MINING.CLASSIFICATION,
  data_table_name      => 'census_2d_build',
  case_id_column_name  => 'person_id',
  target_column_name   => 'class',
  settings_table_name  => 'census_settings');
END;
/

You use similar code to build a One-Class SVM model. The main difference is that the target column is empty.

/* prepare a settings table to override default
 * settings (Naive Bayes is the default classifier)
 */
CREATE TABLE census_settings (
  setting_name  VARCHAR2(30),
  setting_value VARCHAR2(128));

BEGIN
/* indicate that SVM is the chosen classifier */
INSERT INTO census_settings VALUES (
DBMS_DATA_MINING.ALGO_NAME, DBMS_DATA_MINING.ALGO_SUPPORT_VECTOR_MACHINES);

/* override the default value for outlier rate */
INSERT INTO census_settings (setting_name, setting_value)
VALUES (dbms_data_mining.svms_outlier_rate, TO_CHAR(0.05));
COMMIT;

/* build a model with name census_model */
DBMS_DATA_MINING.CREATE_MODEL(
  model_name           => 'census_model',
  mining_function      => DBMS_DATA_MINING.CLASSIFICATION,
  data_table_name      => 'census_2d_build',
  case_id_column_name  => 'person_id',
  target_column_name   => NULL,
  settings_table_name  => 'census_settings');
END;
/

DROP_MODEL Procedure

This procedure drops an existing mining model from the user's schema.

Syntax

DBMS_DATA_MINING.DROP_MODEL (model_name IN VARCHAR2);

Parameters

Usage Notes

You can use DROP_MODEL to drop an existing mining model.

The behavior of the DROP_MODEL is similar to a SQL DDL DROP operation. It blocks RENAME_MODEL and CREATE_MODEL operations. It does not block or block on APPLY, which is a SQL query-like operation that does not update any model data.

If an APPLY operation is using a model, and you attempt to drop the model during that time, the DROP will succeed and APPLY will return indeterminate results. This is in line with the conventional behavior in the RDBMS, where DDL operations do not block on query operations.

Examples

Assume the existence of a model census_model. The following example shows how to drop this model.

BEGIN
  DBMS_DATA_MINING.DROP_MODEL(model_name => 'census_model');
END;
/

EXPORT_MODEL Procedure

This procedure exports the specified data mining models to a dump file set. You can import from the dump file set using the IMPORT_MODEL procedure. Both EXPORT_MODEL and IMPORT_MODEL use Oracle Data Pump technology.

Syntax

DBMS_DATA_MINING.EXPORT_MODEL (
      filename          IN VARCHAR2,
      directory         IN VARCHAR2,
      model_filter      IN VARCHAR2 DEFAULT NULL,
      filesize          IN VARCHAR2 DEFAULT NULL,
      operation         IN VARCHAR2 DEFAULT NULL,
      remote_link       IN VARCHAR2 DEFAULT NULL,
      jobname           IN VARCHAR2 DEFAULT NULL);

Parameters

Usage Notes

The model_filter parameter specifies which models to export. You can list the models by name, or you can identify a group of models that share a given characteristic. To specify models by name, provide a single model name or a comma-delimited list of model names. To specify a group of models that share a characteristic, use a conditional expression that completes the WHERE clause of a query against the DM_USER_MODELS view. DM_USER_MODELS lists the models in the current schema. It has the following columns.

Name                                      Null?    Type
 ----------------------------------------- -------- ----------------------------
 NAME                                      NOT NULL VARCHAR2(25)
 FUNCTION_NAME                                      VARCHAR2(30)
 ALGORITHM_NAME                                     VARCHAR2(30)
 CREATION_DATE                                      DATE
 BUILD_DURATION                                     NUMBER
 TARGET_ATTRIBUTE                                   VARCHAR2(30)
 MODEL_SIZE                                         NUMBER

For descriptions of the columns in DM_USER_MODELS, see "User Views".

To construct a conditional expression for model_filter, specify a column name, a supported conditional operator, and a value. The supported conditional operators are: <, <=, =, =>, >, LIKE, IN. For information on conditional operators and WHERE clauses, see Oracle Database SQL Reference.

Examples of model filters are provided in Table 25-16.

Examples

The following statement exports all the models in the DMUSER3 schema to a dump file set called models_out in the directory $ORACLE_HOME/rdbms/log. This directory is mapped to a directory object called DATA_PUMP_DIR. The DMUSER3 user has read/write access to the directory and to the directory object.

SQL>execute dbms_data_mining.export_model ('models_out', 'DATA_PUMP_DIR');

You can exit SQL*Plus and list the resulting dump file and log file.

SQL>exit
>cd $ORACLE_HOME/rdbms/log
>ls
>DMUSER3_exp_1027.log  models_out01.dmp  

The following example uses the same directory object and is executed by the same user. It exports the models called NMF_SH_SAMPLE and SVMR_SH_REGR_SAMPLE to a different dump file set in the same directory.

SQL>execute dbms_data_mining.export_model ( 'models2_out', 'DATA_PUMP_DIR',
            'name in (''NMF_SH_SAMPLE'', ''SVMR_SH_REGR_SAMPLE'')');
SQL>exit
>cd $ORACLE_HOME/rdbms/log
>ls
>DMUSER3_exp_1027.log  models_out01.dmp
 DMUSER3_exp_924.log  models2_out01.dmp

Using the same directory object and schema, this example exports all models whose target is AFFINITY_CARD.

SQL>execute dbms_data_mining.export_model ('models050402_out', 
                 'DATA_PUMP_DIR', 'target_attribute = ''AFFINITY_CARD''', 
                 '1M', 'EXPORT', NULL, 'models050402_job');
SQL>exit
>cd $ORACLE_HOME/rdbms/log
>ls
>DMUSER3_exp_1027.log  models_out01.dmp
 DMUSER3_exp_924.log   models2_out01.dmp
 models050402_job.log  models050402_out01.dmp   models050402_out02.dmp

GET_ASSOCIATION_RULES Function

This table function returns the rules from an Association model.

You can specify filtering criteria to cause GET_ASSOCIATION_RULES to return a subset of the rules. Filtering criteria can improve the performance of the table function. If the number of rules is large, the greatest performance improvement will result from specifying the topn parameter.

Syntax

DBMS_DATA_MINING.GET_ASSOCIATION_RULES (
   model_name            IN VARCHAR2,
   topn                  IN NUMBER DEFAULT NULL,
   rule_id               IN INTEGER DEFAULT NULL,
   min_confidence        IN NUMBER DEFAULT NULL,
   min_support           IN NUMBER DEFAULT NULL,
   max_rule_length       IN INTEGER DEFAULT NULL,
   min_rule_length       IN INTEGER DEFAULT NULL,
   sort_order            IN DMSYS.ORA_MINING_VARCHAR2_NT DEFAULT NULL,
   antecedent_items      IN DYSYS.ORA_MINING_VARCHAR2_NT DEFAULT NULL,
   consequent_items      IN DYSYS.ORA_MINING_VARCHAR2_NT DEFAULT NULL)
 RETURN DM_RULES PIPELINED;

Parameters

Return Values

(rule_id              INTEGER,
 antecedent           DM_PREDICATES,
 consequent           DM_PREDICATES,
 rule_support         NUMBER,
 rule_confidence      NUMBER,
 antecedent_support   NUMBER,
 consequent_support   NUMBER,
 number_of_items      INTEGER )

(attribute_name            VARCHAR2(30),
      conditional_operator      CHAR(2)/*=,<>,<,>,<=,>=*/,
      attribute_num_value       NUMBER,
      attribute_str_value       VARCHAR2(4000),
      attribute_support         NUMBER,
      attribute_confidence      NUMBER)

Usage Notes

This table function pipes out rows of type DM_RULES. For information on ODM data types and piped output from table functions, see "Data Types".

The DMSYS.ORA_MINING_VARCHAR2_NT type is defined as a table of VARCHAR2(4000).

Examples

The following example demonstrates an Association model build followed by several invocations of the GET_ASSOCIATION_RULES table function.

-- prepare a settings table to override default settings
CREATE TABLE market_settings AS
SELECT *
  FROM TABLE(DBMS_DATA_MINING.GET_DEFAULT_SETTINGS)
 WHERE setting_name LIKE 'ASSO_%';
BEGIN
-- update the value of the minimum confidence
UPDATE census_settings
   SET setting_value = TO_CHAR(0.081)
 WHERE setting_name = DBMS_DATA_MINING.asso_min_confidence;

-- build an AR model 
DBMS_DATA_MINING.CREATE_MODEL(
  model_name => 'market_model',
  function => DBMS_DATA_MINING.ASSOCIATION,
  data_table_name => 'market_build',
  case_id_column_name => 'item_id',
  target_column_name => NULL,
  settings_table_name => 'census_settings');
END;
/
-- View the (unformatted) rules 
SELECT rule_id, antecedent, consequent, rule_support,
       rule_confidence
  FROM TABLE(DBMS_DATA_MINING.GET_ASSOCIATION_RULES('market_model'));

In the previous example, you view all rules. To view just the top 20 rules, use the following statement.

-- View the top 20 (unformatted) rules
SELECT rule_id, antecedent, consequent, rule_support,
       rule_confidence
  FROM TABLE(DBMS_DATA_MINING.GET_ASSOCIATION_RULES('market_model', 20));

The following example returns all the rules which have 'AQUATIC' or 'EGGS' in the antecedent, and has 'VENOMOUS' as the consequent. The rules are sorted first by NUMBER_OF_ITEMS in descending order, then by RULE_CONFIDENCE in descending order, and finally by RULE_SUPPORT in descending order.

SELECT * FROM TABLE
(    DBMS_DATA_MINING.GET_ASSOCIATION_RULES
       ('AR_Model_31', 120, NULL, 1, .51, 7,
         DMSYS.ORA_MINING_VARCHAR2_NT
          ('NUMBER_OF_ITEMS DESC', 'RULE_CONFIDENCE DESC', 'RULE_SUPPORT DESC'),
         DMSYS.ORA_MINING_VARCHAR2_NT('AQUATIC', 'EGGS'),
         DMSYS.ORA_MINING_VARCHAR2_NT('VENOMOUS')));

GET_DEFAULT_SETTINGS Function

This table function returns the default settings for all mining functions and algorithms supported in the DBMS_DATA_MINING package.

Syntax

DBMS_DATA_MINING.GET_DEFAULT_SETTINGS
  RETURN DM_MODEL_SETTINGS PIPELINED;

Return Values

(setting_name    VARCHAR2(30),
 setting_value   VARCHAR2(128))

Usage Notes

This table function pipes out rows of type DM_MODEL_SETTING. For information on ODM data types and ODM data types and piped output from table functions, see "Data Types".

This function is particularly useful if you do not know what settings are associated with a particular function or algorithm, and you want to override some or all of them.

Examples

For example, if you want to override some or all of k-Means clustering settings, you can create a settings table as shown, and update individual settings as required.

BEGIN
  CREATE TABLE mysettings AS
  SELECT * 
  FROM TABLE(DBMS_DATA_MINING.GET_DEFAULT_SETTINGS)
   WHERE setting_name LIKE 'KMNS%';
  -- now update individual settings as required
  UPDATE mysettings
     SET setting_value = 0.02
   WHERE setting_name = DBMS_DATA_MINING.KMNS_MIN_PCT_ATTR_SUPPORT;
END;
/

GET_FREQUENT_ITEMSETS Function

This table function returns a set of rows that represent the frequent itemsets from an Association model. For a detailed description of frequent itemsets, consult Oracle Data Mining Concepts.

Syntax

DBMS_DATA_MINING.GET_FREQUENT_ITEMSETS (
    model_name        IN VARCHAR2,
    topn              IN NUMBER DEFAULT NULL)
  RETURN DM_ITEMSETS PIPELINED;

Parameters

Return Values

(itemsets_id      NUMBER,
items             DM_ITEMS,
support           NUMBER,
number_of_items   NUMBER)

Usage Notes

This table function pipes out rows of type DM_ITEMSETS. For information on ODM data types and piped output from table functions, see "Data Types".

Examples

The following example demonstrates an Association model build followed by an invocation of GET_FREQUENT_ITEMSETS table function from Oracle SQL.

-- prepare a settings table to override default settings
CREATE TABLE market_settings AS

SELECT *

FROM TABLE(DBMS_DATA_MINING.GET_DEFAULT_SETTINGS)
 WHERE setting_name LIKE 'ASSO_%';
BEGIN
-- update the value of the minimum confidence
UPDATE market_settings
   SET setting_value = TO_CHAR(0.081)
 WHERE setting_name = DBMS_DATA_MINING.asso_min_confidence;

/* build a AR model */
DBMS_DATA_MINING.CREATE_MODEL(
  model_name           => 'market_model',
  function             => DBMS_DATA_MINING.ASSOCIATION,
  data_table_name      => 'market_build',
  case_id_column_name  => 'item_id',
  target_column_name   => NULL,
  settings_table_name  => 'census_settings');
END;
/

-- View the (unformatted) Itemsets from SQL*Plus
SELECT itemset_id, items, support, number_of_items
  FROM TABLE(DBMS_DATA_MINING.GET_FREQUENT_ITEMSETS('market_model'));

In the example above, you view all itemsets. To view just the top 20 itemsets, use the following statement:

-- View the top 20 (unformatted) Itemsets from SQL*Plus
SELECT itemset_id, items, support, number_of_items
  FROM TABLE(DBMS_DATA_MINING.GET_FREQUENT_ITEMSETS('market_model', 20));

GET_MODEL_DETAILS_ABN Function

This table function returns a set of rows that provide the details of an Adaptive Bayes Network model.

Syntax

DBMS_DATA_MINING.GET_MODEL_DETAILS_ABN (
    model_name         IN VARCHAR2)
  RETURN DM_ABN_DETAILS PIPELINED;

Parameters

Return Values

(rule_id           INTEGER,
 antecedent        DM_PREDICATES,
 consequent        DM_PREDICATES,
 rule_support      NUMBER)

(attribute_name          VARCHAR2(30),
      conditional_operator    CHAR(2), /*=,<>,<,>,<=,>=*/
      attribute_num_value     NUMBER,
      attribute_str_value     VARCHAR2(4000),
      attribute_support       NUMBER,
      attribute_confidence    NUMBER)

Usage Notes

This table function pipes out rows of type DM_ABN_DETAIL. For information on ODM data types and piped output from table functions, see "Data Types".

This function returns details only for a single feature ABN model.

Examples

The following example demonstrates an ABN model build followed by an invocation of GET_MODEL_DETAILS_ABN table function from Oracle SQL.

BEGIN
  -- prepare a settings table to override default algorithm and model type
  CREATE TABLE abn_settings (setting_name VARCHAR2(30),
  setting_value 
VARCHAR2(128));
  INSERT INTO abn_settings VALUES (DBMS_DATA_MINING.ALGO_NAME,
    DBMS_DATA_MINING.ALGO_ADAPTIVE_BAYES_NETWORK);
  INSERT INTO abn_settings VALUES    (DBMS_DATA_MINING.ABNS_MODEL_TYPE,     DBMS_DATA_MINING.ABNS_SINGLE_FEATURE);
   COMMIT;
  -- create a model
  DBMS_DATA_MINING.CREATE_MODEL (
    model_name           => 'abn_model',
    function             => DBMS_DATA_MINING.CLASSIFICATION,
    data_table_name      => 'abn_build',
    case_id_column_name  => 'id',
    target_column_name   => NULL,
    settings_table_name  => 'abn_settings');
END;
/
-- View the (unformatted) results from SQL*Plus
SELECT *
    FROM TABLE(DBMS_DATA_MINING.GET_MODEL_DETAILS_ABN('abn_model'));

GET_MODEL_DETAILS_AI Function

This table function returns a set of rows that provide the details of an Attribute Importance model.

Syntax

DBMS_DATA_MINING.GET_MODEL_DETAILS_AI (
  model_name         IN VARCHAR2)
 RETURN DM_RANKED_ATTRIBUTES PIPELINED;

Parameters

Return Values

(attribute_name          VARCHAR2(30),
 importance_value        NUMBER,
 rank                    NUMBER(38))

GET_MODEL_DETAILS_KM Function

This table function returns a set of rows that provide the details of a k-Means clustering model.

You can provide input to GET_MODEL_DETAILS_KM to request specific information about the model, thus improving the performance of the query. If you do not specify filtering parameters, GET_MODEL_DETAILS_KM returns all the information about the model.

Syntax

DBMS_DATA_MINING.GET_MODEL_DETAILS_KM (
          model_name         VARCHAR2,
          cluster_id         NUMBER    DEFAULT NULL,
          attribute          VARCHAR2  DEFAULT NULL,
          centroid           NUMBER    DEFAULT 1, 
          histogram          NUMBER    DEFAULT 1, 
          rules              NUMBER    DEFAULT 2)
RETURN DM_CLUSTERS PIPELINED;

Parameters

Return Values

(id                   INTEGER,
 record_count         NUMBER,
 parent               NUMBER,
 tree_level           NUMBER,
 dispersion           NUMBER,
 split_predicate      DM_PREDICATES,
 child                DM_CHILDREN,
 centroid             DM_CENTROIDS,
 histogram            DM_HISTOGRAMS,
 rule                 DM_RULE)

(attribute_name           VARCHAR2(30),
      conditional_operator     CHAR(2) /*=,<>,<,>,<=,>=*/,
      attribute_num_value      NUMBER,
      attribute_str_value      VARCHAR2(4000),
      attribute_support        NUMBER,
      attribute_confidence     NUMBER)

(attribute_name   VARCHAR2(30),
      mean             NUMBER,
      mode_value       VARCHAR2(4000),
      variance         NUMBER)

(attribute_name    VARCHAR2(30),
      bin_id            NUMBER,
      lower_bound       NUMBER,
      upper_bound       NUMBER,
      label             VARCHAR2(4000),
      count             NUMBER)

(rule_id            INTEGER,
      antecedent         DM_PREDICATES,
      consequent         DM_PREDICATES,
      rule_support       NUMBER,
      rule_confidence    NUMBER)

(attribute_name           VARCHAR2(30),
           conditional_operator     CHAR(2)/*=,<>,<,>,<=,>=*/,
           attribute_num_value      NUMBER,
           attribute_str_value      VARCHAR2(4000),
           attribute_support        NUMBER,
           attribute_confidence     NUMBER)

Usage Notes

The table function pipes out rows of type DM_CLUSTERS. For information on ODM data types and piped output from table functions, see "Data Types".

Examples

The following example demonstrates a k-Means clustering model build followed by an invocation of GET_MODEL_DETAILS_KM table function from Oracle SQL.

BEGIN
-- create a settings table
UPDATE cluster_settings
   SET setting_value = 3 
 WHERE setting_name = DBMS_DATA_MINING.KMEANS_BLOCK_GROWTH;

/* build a k-Means clustering model */
DBMS_DATA_MINING.CREATE_MODEL(
  model_name           => 'eight_clouds',
  function             => DBMS_DATA_MINING.CLUSTERING,
  data_table_name      => 'eight_clouds_build',
  case_id_column_name  => 'id',
  target_column_name   => NULL,
  settings_table_name  => 'cluster_settings');
END;
/

-- View the (unformatted) rules from SQL*Plus
SELECT id, record_count, parent, tree_level, dispersion,
       child, centroid, histogram, rule
  FROM TABLE(DBMS_DATA_MINING_GET_MODEL_DETAILS_KM('eight_clouds'));

GET_MODEL_DETAILS_NB Function

This table function returns a set of rows that provide the details of a Naive Bayes model.

Syntax

DBMS_DATA_MINING.GET_MODEL_DETAILS_NB (
   model_name      IN       VARCHAR2)
 RETURN DM_NB_DETAILS PIPELINED;

Parameters

Return Values

(target_attribute_name          VARCHAR2(30),
 target_attribute_str_value     VARCHAR2(4000),
 target_attribute_num_value     NUMBER,
 prior_probability              NUMBER,
 conditionals                   DM_CONDITIONALS)

(attribute_name             VARCHAR2(30),
      attribute_str_value        VARCHAR2(4000),
      attribute_num_value        NUMBER,
      conditional_probability    NUMBER)

Usage Notes

The table function pipes out rows of type DM_NB_DETAILS. For information on ODM data types and piped output from table functions, see "Data Types".

Examples

Assume that you have built a classification model census_model using the Naive Bayes algorithm. You can retrieve the model details as shown in this example.

-- You can view the Naive Bayes model details in many ways
-- Consult the Oracle Application Developer's Guide -
-- Object-Relational Features for different ways of 
-- accessing Oracle Objects.

-- View the (unformatted) details from SQL*Plus
SELECT attribute_name, attribute_num_value, attribute_str_value,
       prior_probability, conditionals,
  FROM TABLE(DBMS_DATA_MINING.GET_MODEL_DETAILS_NB('census_model');

See nbdemo.sql for generation of formatted rules.

GET_MODEL_DETAILS_NMF Function

This table function returns a set of rows that provide the details of a Non-Negative Matrix Factorization model.

Syntax

DBMS_DATA_MINING.GET_MODEL_DETAILS_NMF (
   model_name        IN        VARCHAR2)
 RETURN DM_NMF_FEATURE_SET PIPELINED;

Parameters

Return Values

(feature_id       NUMBER,
 attribute_set    DM_NMF_ATTRIBUTE_SET)

(attribute_name    VARCHAR2(30),
      attribute_value   VARCHAR2(4000),
      coefficient       NUMBER)

Usage Notes

The table function pipes out rows of type DM_NMF_FEATURE_SET. For information on ODM data types and piped output from table functions, see "Data Types".

Examples

Assume you have built an NMF model called my_nmf_model. You can retrieve model details as shown:

--View (unformatted) details from SQL*Plus 
SELECT feature_id, attribute_set 
FROM TABLE(DBMS_DATA_MINING.GET_MODEL_DETAILS_NMF(
        'my_nmf_model'));

GET_MODEL_DETAILS_OC Function

This table function returns a set of rows that provide the details of an O-Cluster clustering model. The rows are an enumeration of the clustering patterns generated during the creation of the model.

You can provide input to GET_MODEL_DETAILS_OC to request specific information about the model, thus improving the performance of the query. If you do not specify filtering parameters, GET_MODEL_DETAILS_OC returns all the information about the model.

Syntax

DBMS_DATA_MINING.GET_MODEL_DETAILS_OC (
          model_name         VARCHAR2,
          cluster_id         NUMBER    DEFAULT NULL,
          attribute          VARCHAR2  DEFAULT NULL,
          centroid           NUMBER    DEFAULT 1, 
          histogram          NUMBER    DEFAULT 1, 
          rules              NUMBER    DEFAULT 2)
RETURN DM_CLUSTERS PIPELINED;

Parameters

Return Values

(id               INTEGER,
 record_count     NUMBER,
 parent           NUMBER,
 tree_level       NUMBER,
 dispersion       NUMBER,
 split_predicate  DM_PREDICATES,
 child            DM_CHILDREN,
 centroid         DM_CENTROIDS,
 histogram        DM_HISTOGRAMS,
 rule             DM_RULE)

(attribute_name           VARCHAR2(30),
      conditional_operator     CHAR(2) /*=,<>,<,>,<=,>=*/,
      attribute_num_value      NUMBER,
      attribute_str_value      VARCHAR2(4000),
      attribute_support        NUMBER,
      attribute_confidence     NUMBER)

(attribute_name   VARCHAR2(30)
       mean             NUMBER,
       mode_value       VARCHAR2(4000),
       variance         NUMBER)

(attribute_name    VARCHAR2(30),
     bin_id            NUMBER,
     lower_bound       NUMBER,
     upper_bound       NUMBER,
     label             VARCHAR2(4000),
     count             NUMBER)

(rule_id           INTEGER,
      antecedent        DM_PREDICATES,
      consequent        DM_PREDICATES,
      rule_support      NUMBER,
      rule_confidence   NUMBER)

(attribute_name           VARCHAR2(30),
           conditional_operator     CHAR(2)/*=,<>,<,>,<=,>=*/,
           attribute_num_value      NUMBER,
           attribute_str_value      VARCHAR2(4000),
           attribute_support        NUMBER,
           attribute_confidence     NUMBER)

Usage Notes

The table function pipes out rows of type DM_CLUSTER. For information about ODM data types and piped output from table functions, see "Data Types".

Examples

Assume you have built an OC model called my_oc_model. You can retrieve information from the model details as shown:

--View (unformatted) details from SQL*Plus 
SELECT T.id           clu_id,
       T.record_count rec_cnt,
       T.parent       parent,
       T.tree_level   tree_level
  FROM (SELECT *
          FROM TABLE(DBMS_DATA_MINING.GET_MODEL_DETAILS_OC(
                 'my_oc_model'))
        ORDER BY id) T
 WHERE ROWNUM < 11;

GET_MODEL_DETAILS_SVM Function

This table function returns a set of rows that provide the details of a Support Vector Machine model. This is applicable only for classification or regression models built using a linear kernel. For any other kernel, the table function returns ORA-40215.

Syntax

DBMS_DATA_MINING.GET_MODEL_DETAILS_SVM (
  model_name      IN       VARCHAR2)
 RETURN DM_SVM_LINEAR_COEFF_SET PIPELINED;

Parameters

Return Values

(class            VARCHAR2(4000),
 attribute_set    DM_SVM_ATTRIBUTE_SET)

(attribute_name      VARCHAR2(30),
      attribute_value     VARCHAR2(4000),
      coefficient         NUMBER)

Usage Notes

The table function pipes out rows of type DM_SVM_LINEAR_COEFF. For information on ODM data types and piped output from table functions, see "Data Types".

The class column of DM_SVM_LINEAR_COEFF represents classification target values. For regression targets, class is NULL. For each classification target value for classification models, a set of coefficients is returned. For binary classification, one-class classifier, and regression models, only a single set of coefficients is returned.

The attribute_value column in the nested table DM_SVM_ATTRIBUTE_SET is used for categorical attributes. The coefficient column is the linear coefficient value.

Examples

The following example demonstrates an SVM model build followed by an invocation of GET_MODEL_DETAILS_SVM table function from Oracle SQL:

-- Create SVM model
BEGIN
  dbms_data_mining.create_model(
    model_name           => 'SVM_Clas_sample',
    mining_function      => dbms_data_mining.classification,
    data_table_name      => 'svmc_sample_build_prepared',
    case_id_column_name  => 'id',
    target_column_name   => 'affinity_card',
    settings_table_name  => 'svmc_sample_settings');
END;
/
-- Display model details
SELECT *
  FROM TABLE(DBMS_DATA_MINING.GET_MODEL_DETAILS_SVM('SVM_Clas_sample'))
ORDER BY class;

GET_MODEL_DETAILS_XML Function

This table function returns an XML object that provides the details of a Decision Tree model.

Syntax

DBMS_DATA_MINING.GET_MODEL_DETAILS_XML (
  model_name      IN       VARCHAR2)
 RETURN XMLTYPE;

Parameters

Return Values

Usage Notes

The function returns the XML representing the decision tree; the definition is the one specified in the Data Mining Group Predictive Model Markup Language (PMML) version 2.1 specification. The specification is available at http://www.dmg.org.

GET_MODEL_SETTINGS Function

This table function returns the list of settings that were used to build the model.

Syntax

DBMS_DATA_MINING.GET_MODEL_SETTINGS(
   model_name           IN VARCHAR2)
 RETURN DM_MODEL_SETTINGS PIPELINED;

Parameters

Return Values

(setting_name    VARCHAR2(30),
setting_value    VARCHAR2(128))

Usage Notes

The table function pipes out rows of type DM_MODEL_SETTING. For information about ODM data types and piped output from table functions, see "Data Types".

You can use this table function to determine the settings that were used to build the model. This is purely for informational purposes only — you cannot alter the model to adopt new settings.

Examples

Assume that you have built a classification model census_model using the Naive Bayes algorithm. You can retrieve the model settings using Oracle SQL as follows:

SELECT setting_name, setting_value
  FROM TABLE(DBMS_DATA_MINING.GET_MODEL_SETTINGS('census_model'));

GET_MODEL_SIGNATURE Function

This table function returns the model signature, which is a set of rows that provide the name and type of each attribute required as input to the APPLY operation.

The case identifier is not considered a mining attribute. For classification and regression models, the target attribute is also not considered part of the model signature.

Syntax

DBMS_DATA_MINING.GET_MODEL_SIGNATURE(
  model_name           IN VARCHAR2)
RETURN DM_MODEL_SIGNATURE PIPELINED;

Parameters

Return Values

(attribute_name      VARCHAR2(30),
 attribute_type      VARCHAR2(106))

Usage Notes

This table function pipes out rows of type DM_MODEL_SIGNATURE. For information on ODM data types and piped output from table functions, see "Data Types".

You can use this table function to get the list of attributes used for building the model. This is particularly helpful to describe a model when an APPLY operation on test or scoring data is done a significant time period after the model is built, or after it is imported into another definition.

Examples

Assume that you have built a classification model census_model using the Naive Bayes algorithm. You can retrieve the model details using Oracle SQL as follows:

SELECT attribute_name, attribute_type
  FROM TABLE(DBMS_DATA_MINING.GET_MODEL_SIGNATURE('census_model');

IMPORT_MODEL Procedure

This procedure imports the specified data mining models from a dump file set that was created with EXPORT_MODEL or with the expdp export utility. Both IMPORT_MODEL and EXPORT_MODEL use Oracle Data Pump technology.

Syntax

DBMS_DATA_MINING.IMPORT_MODEL (
    filename             IN  VARCHAR2,
    directory            IN  VARCHAR2,
    model_filter         IN  VARCHAR2 DEFAULT NULL,
    operation            IN  VARCHAR2 DEFAULT NULL,
    remote_link          IN  VARCHAR2 DEFAULT NULL,
    jobname              IN  VARCHAR2 DEFAULT NULL,
    schema_remap         IN  VARCHAR2 DEFAULT NULL);

Parameters

Usage Notes

The model_filter parameter specifies which models to import. You can list the models by name, or you can identify a group of models that share a given characteristic. To specify models by name, provide a single model name or a comma-delimited list of model names. To specify a group of models that share a characteristic, use a conditional expression that completes the WHERE clause of a query against the DM_USER_MODELS view. DM_USER_MODELS lists the models in the current schema. It has the following columns.

Name                                      Null?    Type
 ----------------------------------------- -------- ----------------------------
 NAME                                      NOT NULL VARCHAR2(25)
 FUNCTION_NAME                                      VARCHAR2(30)
 ALGORITHM_NAME                                     VARCHAR2(30)
 CREATION_DATE                                      DATE
 BUILD_DURATION                                     NUMBER
 TARGET_ATTRIBUTE                                   VARCHAR2(30)
 MODEL_SIZE                                         NUMBER

For descriptions of the columns in DM_USER_MODELS, see "User Views".

To construct a conditional expression for model_filter, specify a column name, a supported conditional operator, and a value. The supported conditional operators are: <, <=, =, =>, >, LIKE, IN. For information on conditional operators and WHERE clauses, see Oracle Database SQL Reference

Examples of model filters are provided in Table 25-43.

Examples

This example shows a model being exported and imported within the schema dmuser2. Then the same model is imported into the dmuser3 schema. The dmuser3 user has the IMPORT_FULL_DATABASE privilege.

SQL>CONNECT dmuser2/dmuser2_psw
SQL>SELECT name FROM dm_user_models;
    NAME
    -------------------------
    NMF_SH_SAMPLE
    SVMO_SH_CLAS_SAMPLE
    SVMR_SH_REGR_SAMPLE

-- export the model called NMF_SH_SAMPLE to a dump file in same schema
SQL>EXECUTE DBMS_DATA_MINING.EXPORT_MODEL ('NMF_SH_SAMPLE_out', 'DATA_PUMP_DIR',
                            'name = ''NMF_SH_SAMPLE''');
-- import the model back into the same schema
SQL>EXECUTE DBMS_DATA_MINING.IMPORT_MODEL ('NMF_SH_SAMPLE_out01.dmp', 
                            'DATA_PUMP_DIR', 'name = ''NMF_SH_SAMPLE''');

-- connect as different user
-- import same model into that schema
SQL>CONNECT dmuser3/dmuser3_psw
SQL>EXECUTE DBMS_DATA_MINING.IMPORT_MODEL ('NMF_SH_SAMPLE_out01.dmp', 
                            'DATA_PUMP_DIR', 'name = ''NMF_SH_SAMPLE''',
                            'IMPORT', NULL, 'nmf_imp_job', 'dmuser2:dmuser3');

The following example shows user MARY importing all models from a dump file, model_exp_001.dmp, which was created by user SCOTT. The dump file is located in the file system directory mapped to a directory object called DM_DUMP. If user MARY does not have IMPORT_FULL_DATABASE privileges, IMPORT_MODEL will raise an error.

-- import all models
DECLARE
  file_name       VARCHAR2(40);
BEGIN
  file_name := 'model_exp_001.dmp';
  DBMS_DATA_MINING.IMPORT_MODEL(
                filename=>file_name,
               directory=>'DM_DUMP',                                          schema_remap=>'SCOTT:MARY');
  DBMS_OUTPUT.PUT_LINE(
'DBMS_DATA_MINING.IMPORT_MODEL of all models from SCOTT done!');
END;
/

RANK_APPLY Procedure

This procedure ranks the results of an APPLY operation based on a top-N specification for predictive and descriptive model results. For classification models, you can provide a cost matrix as input, and obtain the ranked results with costs applied to the predictions.

Syntax

DBMS_DATA_MINING.RANK_APPLY (
      apply_result_table_name        IN VARCHAR2,
      case_id_column_name            IN VARCHAR2,
      score_column_name              IN VARCHAR2,
      score_criterion_column_name    IN VARCHAR2,
      ranked_apply_result_tab_name   IN VARCHAR2,
      top_N                          IN INTEGER DEFAULT 1,
      cost_matrix_table_name         IN VARCHAR2 DEFAULT NULL,
      apply_result_schema_name       IN VARCHAR2 DEFAULT NULL,
      cost_matrix_schema_name        IN VARCHAR2 DEFAULT NULL);

Parameters

Usage Notes

You can use RANK_APPLY to generate ranked apply results, based on a top-N filter and also with application of cost for predictions, if the model was built with costs.

The behavior of RANK_APPLY is similar to that of APPLY with respect to other DDL-like operations such as CREATE_MODEL, DROP_MODEL, and RENAME_MODEL. The procedure does not depend on the model; the only input of relevance is the apply results generated in a fixed schema table from APPLY.

The main intended use of RANK_APPLY is for the generation of the final APPLY results against the scoring data in a production setting. You can apply the model against test data using APPLY, compute various test metrics against various cost matrix tables, and use the candidate cost matrix for RANK_APPLY.

The schema for the apply results from each of the supported algorithms is listed in subsequent sections. The case_id column will be the same case identifier column as that of the apply results.

Classification Models — NB, ABN, SVM

For numerical targets, the ranked results table will have the definition as shown:

(case_id       VARCHAR2/NUMBER,
prediction     NUMBER,
probability    NUMBER,
cost           NUMBER,
rank           INTEGER)

For categorical targets, the ranked results table will have the following definition:

(case_id       VARCHAR2/NUMBER,
prediction     VARCHAR2,
probability    NUMBER,
cost           NUMBER,
rank           INTEGER)

Clustering using k-Means or O-Cluster

Clustering is an unsupervised mining function, and hence there are no targets. The results of an APPLY operation contains simply the cluster identifier corresponding to a case, and the associated probability. Cost matrix is not considered here. The ranked results table will have the definition as shown, and contains the cluster ids ranked by top-N.

(case_id       VARCHAR2/NUMBER,
cluster_id     NUMBER,
probability    NUMBER,
rank           INTEGER)

Feature Extraction using NMF

Feature extraction is also an unsupervised mining function, and hence there are no targets. The results of an APPLY operation contains simply the feature identifier corresponding to a case, and the associated match quality. Cost matrix is not considered here. The ranked results table will have the definition as shown, and contains the feature ids ranked by top-N.

(case_id        VARCHAR2/NUMBER,
feature_id      NUMBER,
match_quality   NUMBER,
rank            INTEGER)

Examples

BEGIN
/* build a model with name census_model.
 * (See example under CREATE_MODEL)
 */ 

/* if build data was pre-processed in any manner,
 * perform the same pre-processing steps on apply
 * data also.
 * (See examples in the section on DBMS_DATA_MINING_TRANSFORM)
 */

/* apply the model to data to be scored */
DBMS_DATA_MINING.RANK_APPLY(
  apply_result_table_name       => 'census_apply_result',
  case_id_column_name           => 'person_id',
  score_column_name             => 'prediction',
  score_criterion_column_name   => 'probability
  ranked_apply_result_tab_name  => 'census_ranked_apply_result',
  top_N                         => 3,
  cost_matrix_table_name        => 'census_cost_matrix');
END;
/

-- View Ranked Apply Results
SELECT *
  FROM census_ranked_apply_result;

RENAME_MODEL Procedure

This procedure renames a mining model to a specified new name.

Syntax

DBMS_DATA_MINING.RENAME_MODEL (
     model_name            IN VARCHAR2,
     new_model_name        IN VARCHAR2);

Parameters

Usage Notes

You can use RENAME_MODEL to rename an existing mining model.

The behavior of the RENAME_MODEL is similar to a SQL DDL RENAME operation. It blocks DROP_MODEL and CREATE_MODEL operations. It does not block APPLY, which is a SQL query-like operation that does not update any model data.

If an APPLY operation is using a model, and you attempt to rename the model during that time, the RENAME will succeed and APPLY will return indeterminate results. This is in line with the conventional behavior in the RDBMS, where DDL operations do not block on query operations.

Examples

Assume the existence of a model census_model. The following example shows how to rename this model.

BEGIN
  DBMS_DATA_MINING.RENAME_MODEL(
    model_name      => 'census_model',
    new_model_name  => 'census_new_model');
END;
/