A Clustering Preserving Transformation for k-Means Algorithm Output

Abstract

This note introduces a novel clustering preserving transformation of cluster sets obtained from k-means algorithm. This transformation may be used to generate new labeled datasets from existent ones. It is more flexible that Kleinberg axiom based consistency transformation because data points in a cluster can be moved away and datapoints between clusters may come closer together.

Tasks

Reproductions