28.3 Data Mining PL/SQL Packages
The PL/SQL interface to Oracle Data Mining is implemented in three packages.
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28.3.1 DBMS_DATA_MINING
Understand the routines of DBMS_DATA_MINING
package.
The DBMS_DATA_MINING
package contains routines for creating mining models, for performing operations on mining models, and for querying mining models. The package includes routines for:
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Creating, dropping, and performing other DDL operations on mining models
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Obtaining detailed information about model attributes, rules, and other information internal to the model (model details)
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Computing test metrics for classification models
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Specifying costs for classification models
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Exporting and importing models
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Building models using Oracle's native algorithms as well as algorithms written in R
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28.3.2 DBMS_DATA_MINING_TRANSFORM
Understand the routines of DBMS_DATA_MINING_TRANSFORM
package.
The DBMS_DATA_MINING_TRANSFORM
package contains routines that perform data transformations such as binning, normalization, and outlier treatment. The package includes routines for:
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Specifying transformations in a format that can be embedded in a mining model.
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Specifying transformations as relational views (external to mining model objects).
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Specifying distinct properties for columns in the build data. For example, you can specify that the column must be interpreted as unstructured text, or that the column must be excluded from Automatic Data Preparation.
28.3.2.1 Transformation Methods in DBMS_DATA_MINING_TRANSFORM
Summarizes the methods for transforming data in DBMS_DATA_MINING_TRANSFORM package.
Table 28-3 DBMS_DATA_MINING_TRANSFORM Transformation Methods
Transformation Method | Description |
---|---|
|
|
|
|
|
Specifies transformations for embedding in a model |
The statements in the following example create an Support Vector Machine (SVM) Classification model called T_SVM_Clas_sample
with an embedded transformation that causes the comments attribute to be treated as unstructured text data.
Example 28-7 Sample Embedded Transformation
DECLARE xformlist dbms_data_mining_transform.TRANSFORM_LIST; BEGIN dbms_data_mining_transform.SET_TRANSFORM( xformlist, 'comments', null, 'comments', null, 'TEXT'); DBMS_DATA_MINING.CREATE_MODEL( model_name => 'T_SVM_Clas_sample', mining_function => dbms_data_mining.classification, data_table_name => 'mining_build_text', case_id_column_name => 'cust_id', target_column_name => 'affinity_card', settings_table_name => 't_svmc_sample_settings', xform_list => xformlist); END; /
28.3.3 DBMS_PREDICTIVE_ANALYTICS
Understand the routines of DBMS_PREDICTIVE_ANALYTICS
package.
The DBMS_PREDICTIVE_ANALYTICS
package contains routines that perform an automated form of data mining known as predictive analytics. With predictive analytics, you do not need to be aware of model building or scoring. All mining activities are handled internally by the procedure. The DBMS_PREDICTIVE_ANALYTICS
package includes these routines:
-
EXPLAIN
ranks attributes in order of influence in explaining a target column. -
PREDICT
predicts the value of a target column based on values in the input data. -
PROFILE
generates rules that describe the cases from the input data.
The EXPLAIN
statement in the following example lists attributes in the view mining_data_build_v
in order of their importance in predicting affinity_card
.
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