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Description of the illustration ''cluster_set.gif''


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Description of the illustration ''mining_attribute_clause.gif''


This function is for use with clustering models created by the DBMS_DATA_MINING package or with Oracle Data Miner. It returns a varray of objects containing all possible clusters that a given row belongs to. Each object in the varray is a pair of scalar values containing the cluster ID and the cluster probability. The object fields are named CLUSTER_ID and PROBABILITY, and both are Oracle NUMBER.

  • For the optional topN argument, specify a positive integer. Doing so restricts the set of predicted clusters to those that have one of the top N probability values. If you omit topN or set it to NULL, then all clusters are returned in the collection. If multiple clusters are tied for the Nth value, the database still returns only N values.

  • For the optional cutoff argument, specify a positive integer to restrict the returned clusters to those with a probability greater than or equal to the specified cutoff. You can filter only by cutoff by specifying NULL for topN and the desired cutoff value for cutoff.

You can specify topN and cutoff together to restrict the returned clusters to those that are in the top N and have a probability that passes the threshold.

The mining_attribute_clause behaves as described for the PREDICTION function. Refer to mining_attribute_clause.

See Also:


The following example lists the most relevant attributes (with confidence > 55%) of each cluster to which customer 101362 belongs with > 20% likelihood.

This example, and the prerequisite data mining operations, including the creation of the km_sh_clus_sample model and the views and type, can be found in the demo file $ORACLE_HOME/rdbms/demo/dmkmdemo.sql. General information on data mining demo files is available in Oracle Data Mining Administrator's Guide. The example is presented here to illustrate the syntactic use of the function.

clus_tab AS (
       A.attribute_name aname,
       A.conditional_operator op,
         ROUND(A.attribute_num_value),4)) val,
       A.attribute_support support,
       A.attribute_confidence confidence
       TABLE(T.rule.antecedent) A
 WHERE A.attribute_confidence > 0.55
clust AS (
       CAST(COLLECT(Cattr(aname, op, TO_CHAR(val), support, confidence))
         AS Cattrs) cl_attrs
  FROM clus_tab
custclus AS (
SELECT T.cust_id, S.cluster_id, S.probability
  FROM (SELECT cust_id, CLUSTER_SET(km_sh_clus_sample, NULL, 0.2 USING *) pset
          FROM mining_data_apply_v
         WHERE cust_id = 101362) T,
       TABLE(T.pset) S
SELECT A.probability prob, A.cluster_id cl_id,
       B.attr, B.op, B.val, B.supp, B.conf
  FROM custclus A,
       (SELECT, C.*
          FROM clust T,
               TABLE(T.cl_attrs) C) B
 WHERE A.cluster_id =
ORDER BY prob DESC, cl_id ASC, conf DESC, attr ASC, val ASC;

   PROB      CL_ID ATTR                       OP VAL             SUPP    CONF
------- ---------- -------------------------- -- ----------- -------- -------
  .7745          8 HOUSEHOLD_SIZE             IN 9+               124   .7500
  .7745          8 CUST_MARITAL_STATUS        IN Divorc.          116   .6000
  .7745          8 CUST_MARITAL_STATUS        IN NeverM           116   .6000
  .7745          8 CUST_MARITAL_STATUS        IN Separ.           116   .6000
  .7745          8 CUST_MARITAL_STATUS        IN Widowed          116   .6000
  .2028          6 AGE                        >= 17               154   .6667
  .2028          6 AGE                        <= 31.6             154   .6667
  .2028          6 CUST_MARITAL_STATUS        IN NeverM           172   .6667
8 rows selected.