CLUSTER_DISTANCE

Syntax

cluster_distance::=

Description of the illustration cluster_distance.eps

Analytic Syntax

cluster_distance_analytic::=

Description of the illustration cluster_distance_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_DISTANCE returns a cluster distance for each row in the selection. The cluster distance is the distance between the row and the centroid of the highest probability cluster or the specified cluster_id. The distance is returned as BINARY_DOUBLE.

Syntax Choice

CLUSTER_DISTANCE 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_DISTANCE 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, this data is 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

This example finds the 10 rows that are most anomalous as measured by their distance from their nearest cluster centroid.

SELECT cust_id
  FROM (
    SELECT cust_id,
           rank() over
             (order by CLUSTER_DISTANCE(km_sh_clus_sample USING *) desc) rnk
      FROM mining_data_apply_v)
  WHERE rnk <= 11
  ORDER BY rnk;
 
   CUST_ID
----------
    100579
    100050
    100329
    100962
    101251
    100179
    100382
    100713
    100629
    100787
    101478