CLUSTER_PROBABILITY

Syntax

cluster_probability::=

Description of the illustration cluster_probability.eps

Analytic Syntax

cluster_prob_analytic::=

Description of the illustration cluster_prob_analytic.eps

mining_attribute_clause::=

Description of the illustration mining_attribute_clause.eps

mining_analytic_clause::=

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_PROBABILITY returns a probability for each row in the selection. The probability refers to the highest probability cluster or to the specified cluster_id. The cluster probability is returned as BINARY_DOUBLE.

Syntax Choice

CLUSTER_PROBABILITY 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_PROBABILITY 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 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

The following example lists the ten most representative customers, based on likelihood, of cluster 2.

SELECT cust_id
  FROM (SELECT cust_id, rank() OVER (ORDER BY prob DESC, cust_id) rnk_clus2
    FROM (SELECT cust_id, CLUSTER_PROBABILITY(km_sh_clus_sample, 2 USING *) prob
          FROM mining_data_apply_v))
WHERE rnk_clus2 <= 10
ORDER BY rnk_clus2;
 
   CUST_ID
----------
    100256
    100988
    100889
    101086
    101215
    100390
    100985
    101026
    100601
    100672