CLUSTER_ID

Syntax

cluster_id::=

Description of the illustration cluster_id.gif

Description of the illustration cluster_id.eps

Analytic Syntax

cluster_id_analytic::=

Description of the illustration cluster_id_analytic.gif

Description of the illustration cluster_id_analytic.eps

mining_attribute_clause::=

Description of the illustration mining_attribute_clause.gif

Description of the illustration mining_attribute_clause.eps

mining_analytic_clause::=

Description of the illustration mining_analytic_clause.gif

Description of the illustration mining_analytic_clause.eps

See Also: Analytic Functions for information on the syntax, semantics, and restrictions of mining_analytic_clause

Purpose

CLUSTER_ID returns the identifier of the highest probability cluster for each row in the selection. The cluster identifier is returned as an Oracle NUMBER.

Syntax Choice

CLUSTER_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 clustering 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 clusters to compute, 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::=.)

The syntax of the CLUSTER_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 clustering.

Note: The following examples are excerpted from the Data Mining sample programs. For more information about the sample programs, see in Oracle Data Mining User’s Guide.

Example

The following example lists the clusters into which the customers in mining_data_apply_v have been grouped.

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

      CLUS        CNT
---------- ----------
         2        580
        10        216
         6        186
         8        115
        19        110
        12        101
        18         81
        16         39
        17         38
        14         34

Analytic Example

This example divides the customer database into four segments based on common characteristics. The clustering functions compute the clusters and return the score without a predefined clustering model.

SELECT * FROM (
     SELECT cust_id,
          CLUSTER_ID(INTO 4 USING *) OVER () cls,
          CLUSTER_DETAILS(INTO 4 USING *) OVER () cls_details
     FROM mining_data_apply_v)
WHERE cust_id <= 100003
ORDER BY 1;

CUST_ID CLS CLS_DETAILS
------- --- -----------------------------------------------------------------------------
 100001   5 <Details algorithm="K-Means Clustering" cluster="5">
            <Attribute name="FLAT_PANEL_MONITOR" actualValue="0" weight=".349" rank="1"/>
            <Attribute name="BULK_PACK_DISKETTES" actualValue="0" weight=".33" rank="2"/>
            <Attribute name="CUST_INCOME_LEVEL" actualValue="G: 130\,000 - 149\,999"
               weight=".291" rank="3"/>
            <Attribute name="HOME_THEATER_PACKAGE" actualValue="1" weight=".268" rank="4"/>
            <Attribute name="Y_BOX_GAMES" actualValue="0" weight=".179" rank="5"/>
            </Details>

 100002   6 <Details algorithm="K-Means Clustering" cluster="6">
            <Attribute name="CUST_GENDER" actualValue="F" weight=".945" rank="1"/>
            <Attribute name="CUST_MARITAL_STATUS" actualValue="NeverM" weight=".856" rank="2"/>
            <Attribute name="HOUSEHOLD_SIZE" actualValue="2" weight=".468" rank="3"/>
            <Attribute name="AFFINITY_CARD" actualValue="0" weight=".012" rank="4"/>
            <Attribute name="CUST_INCOME_LEVEL" actualValue="L: 300\,000 and above"
               weight=".009" rank="5"/>
            </Details>

 100003   7 <Details algorithm="K-Means Clustering" cluster="7">
            <Attribute name="CUST_MARITAL_STATUS" actualValue="NeverM" weight=".862" rank="1"/>
            <Attribute name="HOUSEHOLD_SIZE" actualValue="2" weight=".423" rank="2"/>
            <Attribute name="HOME_THEATER_PACKAGE" actualValue="0" weight=".113" rank="3"/>
            <Attribute name="AFFINITY_CARD" actualValue="0" weight=".007" rank="4"/>
            <Attribute name="CUST_ID" actualValue="100003" weight=".006" rank="5"/>
            </Details>