CLUSTER_PROBABILITY

Syntax

cluster_probability::=

Description of the illustration cluster_probability.gif

Analytic Syntax

cluster_prob_analytic::=

Description of the illustration cluster_prob_analytic.gif

mining_attribute_clause::=

Description of the illustration mining_attribute_clause.gif

mining_analytic_clause::=

Description of the illustration mining_analytic_clause.gif

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 Machine Learning for SQL User’s Guide for information about scoring.

• Oracle Machine Learning for SQL Concepts for information about clustering.

Note: The following example is excerpted from the Oracle Machine Learning for SQL sample programs. For more information about the sample programs, see in Oracle Machine Learning for SQL User’s Guide.

Example

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

rank