Data Mining SQL Scoring Functions

The Data Mining SQL language functions use Oracle Data Mining to score data. The functions can apply a mining model schema object to the data, or they can dynamically mine the data by executing an analytic clause. SQL functions are available for all the data mining algorithms that support the scoring operation. Table 2-5 lists the Data Mining SQL functions.

Table 2-5 Data Mining SQL Functions

Function	Description
`CLUSTER_ID`	Returns the ID of the predicted cluster
`CLUSTER_DETAILS`	Returns detailed information about the predicted cluster
`CLUSTER_DISTANCE`	Returns the distance from the centroid of the predicted cluster
`CLUSTER_PROBABILITY`	Returns the probability of a case belonging to a given cluster
`CLUSTER_SET`	Returns a list of all possible clusters to which a given case belongs along with the associated probability of inclusion
`FEATURE_ID`	Returns the ID of the feature with the highest coefficient value
`FEATURE_DETAILS`	Returns detailed information about the predicted feature
`FEATURE_SET`	Returns a list of objects containing all possible features along with the associated coefficients
`FEATURE_VALUE`	Returns the value of the predicted feature
`PREDICTION`	Returns the best prediction for the target
`PREDICTION_BOUNDS`	(GLM only) Returns the upper and lower bounds of the interval wherein the predicted values (linear regression) or probabilities (logistic regression) lie.
`PREDICTION_COST`	Returns a measure of the cost of incorrect predictions
`PREDICTION_DETAILS`	Returns detailed information about the prediction
`PREDICTION_PROBABILITY`	Returns the probability of the prediction
`PREDICTION_SET`	Returns the results of a classification model, including the predictions and associated probabilities for each case

Example 2-7 shows a query that returns the results of the CLUSTER_ID function. The query applies the model em_sh_clus_sample, which finds groups of customers that share certain characteristics. The query returns the identifiers of the clusters and the number of customers in each cluster.

Example 2-7 CLUSTER_ID Function

-- -List the clusters into which the customers in this
-- -data set have been grouped.
--
SELECT CLUSTER_ID(em_sh_clus_sample USING *) AS clus, COUNT(*) AS cnt 
  FROM mining_data_apply_v
GROUP BY CLUSTER_ID(em_sh_clus_sample USING *)
ORDER BY cnt DESC;


SQL> -- List the clusters into which the customers in this
SQL> -- data set have been grouped.
SQL> --
SQL> SELECT CLUSTER_ID(em_sh_clus_sample USING *) AS clus, COUNT(*) AS cnt
  2    FROM mining_data_apply_v
  3  GROUP BY CLUSTER_ID(em_sh_clus_sample USING *)
  4  ORDER BY cnt DESC;
 
      CLUS        CNT
---------- ----------
         9        311
         3        294
         7        215
        12        201
        17        123
        16        114
        14         86
        19         64
        15         56
        18         36