A Clustering Preserving Transformation for k-Means Algorithm Output
Machine Learning
2022-07-26 v2
Abstract
This note introduces a novel clustering preserving transformation of cluster sets obtained from -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.
Keywords
Cite
@article{arxiv.2202.10455,
title = {A Clustering Preserving Transformation for k-Means Algorithm Output},
author = {Mieczysław A. Kłopotek},
journal= {arXiv preprint arXiv:2202.10455},
year = {2022}
}
Comments
14 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