mlsanityData to 7 Oct 2026

A Clustering Preserving Transformation for k-Means Algorithm Output

arXiv 2202.10455 ↗First posted 19 Feb 2022cs.LGcited by 4

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 3 of its 18 references that are in the map. First posted on arXiv 19 Feb 2022.

Builds on

3 of 18 references.

References · 3

  1. 2016Heavy hitters via cluster-preserving clusteringcited by 10
  2. 2022Towards Continuous Consistency Axiomcited by 4
  3. 2020Privacy Preserving K-Means Clustering: A Secure Multi-Party Computation Approachcited by 2

Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 2.

4 papers.

  1. 2022How To Overcome Richness Axiom Fallacycited by 0
  2. 2022High-Dimensional Wide Gap k-Means Versus Clustering Axiomscited by 0
  3. 2023Are Easy Data Easy (for K-Means)cited by 0
  4. 2023Wide Gaps and Clustering Axiomscited by 0