Map Clustering on Labels

Synopsis

This operator converts the cluster attribute into a prediction attribute.

Description

The Map Clustering on Labels operator expects a clustered ExampleSet and a cluster model as input. Using these inputs, it estimates a mapping between the given clustering and prediction. It adjusts the given clusters with the given labels and so estimates the best fitting pairs. The resultant ExampleSet has a prediction attribute which is derived from the cluster attribute.

Input

example set

This input port expects a clustered ExampleSet. It is the output of the K-Means operator in the attached Example Process.

cluster model

This input port expects a cluster model. It is the output of the K-Means operator in the attached Example Process.

Output

example set

The prediction attribute is derived from the cluster attribute and the resultant ExampleSet is delivered through this port.

cluster model

The cluster model that was given as input is passed without any modifications to the output through this port. This is usually used to reuse the same cluster model in further operators or to view the cluster model in the Results Workspace.