Data mining — Results data

The results data from each of the Intelligent Miner® functions is identified by a data type that is specific to Intelligent Miner.

The following table shows the application functions and their data types and results data.

Table 1. Application functions and their data types and results data
Application function	Data type	Results data
`DM_applyClusModel`	`DM_ClusResult`	Cluster ID, score, quality, confidence
`DM_applyClasModel`	`DM_ClasResult`	Predicted class, confidence
`DM_applyRegModel`	`DM_RegResult`	Predicted value Data region ID for an RBF Regression model Confidence, deviation, standard deviation are only available with Transform Regression models

Note: If you create your own DB2® tables to store results, ensure that you include a column that is configured for the appropriate data type.

Figure 1 illustrates the process by which data is selected and a model is applied. Values for the fields age and salary are read from a database table to form an instance of the data type DM_ApplicationData. This data and the model ClusterModel form the input to the function DM_applyClusModel. The results of applying the model consist of a cluster ID and a cluster score. The results are returned as data type DM_ClusResult.

Figure 1. Applying a model to data