Description of feature_value.gif follows
Description of the illustration ''feature_value.gif''

Analytic Syntax


Description of feature_value_analytic.gif follows
Description of the illustration ''feature_value_analytic.gif''


Description of mining_attribute_clause.gif follows
Description of the illustration ''mining_attribute_clause.gif''


Description of mining_analytic_clause.gif follows
Description of the illustration ''mining_analytic_clause.gif''

See Also:

"Analytic Functions" for information on the syntax, semantics, and restrictions of mining_analytic_clause


FEATURE_VALUE returns a feature value for each row in the selection. The value refers to the highest value feature or to the specified feature_id. The feature value is returned as BINARY_DOUBLE.

Syntax Choice

FEATURE_VALUE can score the data in one of two ways: It can apply a mining model object to the data, or it can dynamically mine the data by executing an analytic clause that builds and applies one or more transient mining models. Choose Syntax or Analytic Syntax:

  • Syntax — Use the first syntax to score the data with a pre-defined model. Supply the name of a feature extraction model.

  • Analytic Syntax — Use the analytic syntax to score the data without a pre-defined model. Include INTO n, where n is the number of features to extract, and mining_analytic_clause, which specifies if the data should be partitioned for multiple model builds. The mining_analytic_clause supports a query_partition_clause and an order_by_clause. (See "analytic_clause::=".)


mining_attribute_clause identifies the column attributes to use as predictors for scoring. When the function is invoked with the analytic syntax, this data is also used for building the transient models. The mining_attribute_clause behaves as described for the PREDICTION function. (See "mining_attribute_clause::=".)

See Also:

About the Example:

The following example is excerpted from the Data Mining sample programs. For more information about the sample programs, see Appendix A in Oracle Data Mining User's Guide.


The following example lists the customers that correspond to feature 3, ordered by match quality.

  FROM (SELECT cust_id, FEATURE_VALUE(nmf_sh_sample, 3 USING *) match_quality
          FROM nmf_sh_sample_apply_prepared
          ORDER BY match_quality DESC)

---------- -------------
    100210    19.4101627
    100962    15.2482251
    101151    14.5685197
    101499    14.4186292
    100363    14.4037396
    100372    14.3335148
    100982    14.1716545
    101039    14.1079914
    100759    14.0913761
    100953    14.0799737