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 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.

arXiv comment14 pages, 5 figures; the paper extends the method of consistency transformation discussed in arXiv:2202.06015. arXiv admin note: substantial text overlap with arXiv:2202.06015