Extract Cluster Prototypes (RapidMiner Studio Core)

Synopsis

This operator generates an ExampleSet consisting of the Cluster Prototypes from the Cluster Model. This operator is usually applied after clustering operators to store the Cluster Prototypes in form of an ExampleSet.

Description

Most clustering algorithms like K-Means or K-Medoids cluster the data around some prototypical data vectors. For example the K-Means algorithm uses the centroid of all examples of a cluster. The Extract Cluster Prototypes operator extracts these prototypes and stores them in an ExampleSet for further use. This operator expects a cluster model as input. The information about the cluster prototypes can be seen in the cluster models generated by most clustering operators but the Extract Cluster Prototypes operator stores this information in form of an ExampleSet thus it can be used easily.

Clustering is concerned with grouping together objects that are similar to each other and dissimilar to the objects belonging to other clusters. Clustering is a technique for extracting information from unlabeled data. Clustering can be very useful in many different scenarios e.g. in a marketing application we may be interested in finding clusters of customers with similar buying behavior.

Differentiation

k-Medoids

The K-Medoids operator performs the clustering and generates a cluster model and a clustered ExampleSet. The cluster model generated by the K-Medoids operator can be used by the Extract Cluster Prototypes operator to store the Centroid Table in form of an ExampleSet.