38.8 FEATURE_ID
Syntax
feature_id::=
Analytic Syntax
feature_id_analytic::=
mining_attribute_clause::=
mining_analytic_clause::=
See Also:
"Analytic Functions" for information on the syntax, semantics, and restrictions of mining_analytic_clause
Purpose
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
.
Syntax Choice
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
, wheren
is the number of features to extract, andmining_analytic_clause
, which specifies if the data should be partitioned for multiple model builds. Themining_analytic_clause
supports aquery_partition_clause
and anorder_by_clause
. (See "analytic_clause::=".)
The syntax of the FEATURE_ID
function can use an optional GROUPING
hint when scoring a partitioned model. See GROUPING Hint.
mining_attribute_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.
Note:
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.
Example
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