1. SQL Language Reference
  2. Functions
  3. CLUSTER_DETAILS

CLUSTER_DETAILS

Syntax

cluster_details::=

Description of cluster_details.eps follows
Description of the illustration cluster_details.eps

Analytic Syntax

cluster_details_analytic::=

Description of cluster_details_analytic.eps follows
Description of the illustration cluster_details_analytic.eps

mining_attribute_clause::=

Description of mining_attribute_clause.eps follows
Description of the illustration mining_attribute_clause.eps

mining_analytic_clause::=

Description of mining_analytic_clause.eps follows
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_DETAILS returns cluster details for each row in the selection. The return value is an XML string that describes the attributes of the highest probability cluster or the specified cluster_id.

topN

If you specify a value for topN, the function returns the N attributes that most influence the cluster assignment (the score). If you do not specify topN, the function returns the 5 most influential attributes.

DESC, ASC, or ABS

The returned attributes are ordered by weight. The weight of an attribute expresses its positive or negative impact on cluster assignment. A positive weight indicates an increased likelihood of assignment. A negative weight indicates a decreased likelihood of assignment.

By default, CLUSTER_DETAILS returns the attributes with the highest positive weights (DESC). If you specify ASC, the attributes with the highest negative weights are returned. If you specify ABS, the attributes with the greatest weights, whether negative or positive, are returned. The results are ordered by absolute value from highest to lowest. Attributes with a zero weight are not included in the output.

Syntax Choice

CLUSTER_DETAILS 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_DETAILS 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:

Note:

The following examples are 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 attributes that have the greatest impact (more that 20% probability) on cluster assignment for customer ID 100955. The query invokes the CLUSTER_DETAILS and CLUSTER_SET functions, which apply the clustering model em_sh_clus_sample. 

SELECT S.cluster_id, probability prob,
       CLUSTER_DETAILS(em_sh_clus_sample, S.cluster_id, 5 USING T.*) det
FROM
  (SELECT v.*, CLUSTER_SET(em_sh_clus_sample, NULL, 0.2 USING *) pset
    FROM mining_data_apply_v v
   WHERE cust_id = 100955) T,
  TABLE(T.pset) S
ORDER BY 2 DESC;  
 
CLUSTER_ID  PROB DET
---------- ----- ---------------------------------------------------------------------------------
        14 .6761 <Details algorithm="Expectation Maximization" cluster="14">
                 <Attribute name="AGE" actualValue="51" weight=".676" rank="1"/>
                 <Attribute name="HOME_THEATER_PACKAGE" actualValue="1" weight=".557" rank="2"/>
                 <Attribute name="FLAT_PANEL_MONITOR" actualValue="0" weight=".412" rank="3"/>
                 <Attribute name="Y_BOX_GAMES" actualValue="0" weight=".171" rank="4"/>
                 <Attribute name="BOOKKEEPING_APPLICATION" actualValue="1" weight="-.003"rank="5"/>
                 </Details>
 
         3 .3227 <Details algorithm="Expectation Maximization" cluster="3">
                 <Attribute name="YRS_RESIDENCE" actualValue="3" weight=".323" rank="1"/>
                 <Attribute name="BULK_PACK_DISKETTES" actualValue="1" weight=".265" rank="2"/>
                 <Attribute name="EDUCATION" actualValue="HS-grad" weight=".172" rank="3"/>
                 <Attribute name="AFFINITY_CARD" actualValue="0" weight=".125" rank="4"/>
                 <Attribute name="OCCUPATION" actualValue="Crafts" weight=".055" rank="5"/>
                 </Details>

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>