See Also:"Analytic Functions" for information on the syntax, semantics, and restrictions of
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
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
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::=".)
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::=".)
About the Example: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.
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