# A Clustering Preserving Transformation for k-Means Algorithm Output

arXiv: https://arxiv.org/abs/2202.10455 · HTML: https://mlsanity.com/paper/2202.10455/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 19 Feb 2022.

## Builds on (most cited first, 3 of 3)

- [Heavy hitters via cluster-preserving clustering](https://mlsanity.com/paper/1604.01357/) (2016)
- [Towards Continuous Consistency Axiom](https://mlsanity.com/paper/2202.06015/) (2022)
- [Privacy Preserving K-Means Clustering: A Secure Multi-Party Computation Approach](https://mlsanity.com/paper/2009.10453/) (2020)

## Built on by (most cited first, 4 of 4)

- [How To Overcome Richness Axiom Fallacy](https://mlsanity.com/paper/2210.15507/) (2022)
- [High-Dimensional Wide Gap k-Means Versus Clustering Axioms](https://mlsanity.com/paper/2211.17036/) (2022)
- [Are Easy Data Easy (for K-Means)](https://mlsanity.com/paper/2308.01926/) (2023)
- [Wide Gaps and Clustering Axioms](https://mlsanity.com/paper/2308.03464/) (2023)

## 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.