See Also:
"Analytic Functions" for information on the syntax, semantics, and restrictions ofmining_analytic_clause
FEATURE_ID
returns the identifier of the highest value feature for each row in the selection. The feature identifier is returned as an Oracle NUMBER
.
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 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, 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::=".)
See Also:
Oracle Data Mining User's Guide for information about scoring.
Oracle Data Mining Concepts for information about feature extraction.
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