See Also:"Analytic Functions" for information on the syntax, semantics, and restrictions of
FEATURE_ID returns the identifier of the highest value feature for each row in the selection. The feature identifier is returned as an Oracle
FEATURE_ID 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
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, these predictors are also used for building the transient models. The
mining_attribute_clause behaves as described for the
PREDICTION function. (See "mining_attribute_clause::=".)
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.
This example lists the features and corresponding count of customers in a data set.
SELECT FEATURE_ID(nmf_sh_sample USING *) AS feat, COUNT(*) AS cnt FROM nmf_sh_sample_apply_prepared GROUP BY FEATURE_ID(nmf_sh_sample USING *) ORDER BY cnt DESC, feat DESC; FEAT CNT ---------- ---------- 7 1443 2 49 3 6 6 1 1 1