A Clustering Preserving Transformation for k-Means Algorithm Output

TL;DR

This paper presents a new clustering-preserving transformation for k-means outputs that allows generating new labeled datasets by repositioning data points within and between clusters, offering more flexibility than previous methods.

Contribution

It introduces a novel transformation that preserves clustering structure while enabling flexible data manipulation, unlike Kleinberg's consistency axiom-based methods.

Findings

Transformation maintains clustering structure

Allows repositioning within clusters and merging between clusters

Enables generation of new labeled datasets

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 data{}sets 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.