# Topological data analysis and clustering

arXiv: https://arxiv.org/abs/2201.09054 · HTML: https://mlsanity.com/paper/2201.09054/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 32 references that are in the map.
- First posted on arXiv 22 Jan 2022.

## Builds on (most cited first, 1 of 1)

- [Spectral Sequences, Exact Couples and Persistent Homology of filtrations](https://mlsanity.com/paper/1308.0801/) (2013)

## Built on by (most cited first, 2 of 2)

- [k-Means Clustering for Persistent Homology](https://mlsanity.com/paper/2210.10003/) (2022)
- [Topology-Driven Clustering: Enhancing Performance with Betti Number Filtration](https://mlsanity.com/paper/2505.04346/) (2025)

## Abstract (arXiv)

Clustering is one of the most common tasks of Machine Learning. In this paper we examine how ideas from topology can be used to improve clustering techniques.