A Clustering Preserving Transformation for k-Means Algorithm Output

In this note we introduce a novel clustering preserving transformation of cluster sets obtained from k-means algorithm. It may be considered as a contribution towards formulation of clustering axiomatic system. From the practical point of view, this clustering preserving transformation can be used for purposes of: generating new labeled datasets from existent ones, which may be of use in testing algorithms from k-means family in their stability on cluster perturbations which d not change the theoretical clustering, generating new labeled datasets from existent ones, obfuscating sensitive data From the theoretical standpoint, the contribution of this paper consists in proposing a less rigid cluster preserving transformation than centric consistency, known so far as the only cluster preserving transformation for k-means family of algorithms.