# On Coreset Constructions for the Fuzzy K-Means Problem

arXiv: https://arxiv.org/abs/1612.07516 · HTML: https://mlsanity.com/paper/1612.07516/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2016.

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

- [Position: Modular Memory is the Key to Continual Learning Agents](https://mlsanity.com/paper/2603.01761/) (2026)
- [Core Tokensets for Data-efficient Sequential Training of Transformers](https://mlsanity.com/paper/2410.05800/) (2024)

## Abstract (arXiv)

The fuzzy K-means problem is a popular generalization of the well-known K-means problem to soft clusterings. We present the first coresets for fuzzy K-means with size linear in the dimension, polynomial in the number of clusters, and poly-logarithmic in the number of points. We show that these coresets can be employed in the computation of a (1+ε)-approximation for fuzzy K-means, improving previously presented results. We further show that our coresets can be maintained in an insertion-only streaming setting, where data points arrive one-by-one.