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Oracle® Database SQL Reference 10g Release 2 (10.2) Part Number B14200-02 |
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# CLUSTER_ID

Syntax

Description of the illustration cluster_id.gif

mining_attribute_clause::=

Description of the illustration mining_attribute_clause.gif

Purpose

This function is for use with clustering models that have been created using the `DBMS_DATA_MINING` package or with the Oracle Data Mining Java API. It returns the cluster identifier of the predicted cluster with the highest probability for the set of predictors specified in the `mining_attribute_clause`. The value returned is an Oracle `NUMBER`.

The `mining_attribute_clause` behaves as described for the `PREDICTION` function. Please refer to mining_attribute_clause.

See Also: Oracle Data Mining Concepts for detailed information on Oracle Data Mining features Oracle Data Mining Administrator's Guide for information on the demo programs available in the code Oracle Data Mining Application Developer's Guide for information on writing Oracle Data Mining applications PREDICTION

Examples

The following example lists the clusters into which customers of a given dataset have been grouped.

This example, and the prerequisite data mining operations, including the creation of the `dm_sh_clus_sample` model and the `dm_sh_sample_apply_prepared` view, can be found in the demo file `$ORACLE_HOME/rdbms/demo/dmkmdemo.sql`. General information on data mining demo files is available in Oracle Data Mining Administrator's Guide. The example is presented here to illustrate the syntactic use of the function.

    SELECT CLUSTER_ID(km_sh_clus_sample USING *) AS clus, COUNT(*) AS cnt
      FROM km_sh_sample_apply_prepared
    GROUP BY CLUSTER_ID(km_sh_clus_sample USING *)
    ORDER BY cnt DESC;

          CLUS        CNT
    ---------- ----------
             2        580
            10        199
             6        185
             8        115
            12         98
            16         82
            19         81
            15         68
            18         65
            14         27

    10 rows selected.