# Entropy Regularized Power k-Means Clustering

arXiv: https://arxiv.org/abs/2001.03452 · HTML: https://mlsanity.com/paper/2001.03452/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2020.

## Built on by (most cited first, 1 of 1)

- [Towards Objective Obstetric Ultrasound Assessment: Contrastive Representation Learning for Fetal Movement Detection](https://mlsanity.com/paper/2510.20214/) (2025)

## Abstract (arXiv)

Despite its well-known shortcomings, k-means remains one of the most widely used approaches to data clustering. Current research continues to tackle its flaws while attempting to preserve its simplicity. Recently, the textitpower k-means algorithm was proposed to avoid trapping in local minima by annealing through a family of smoother surfaces. However, the approach lacks theoretical justification and fails in high dimensions when many features are irrelevant. This paper addresses these issues by introducing textitentropy regularization to learn feature relevance while annealing. We prove consistency of the proposed approach and derive a scalable majorization-minimization algorithm that enjoys closed-form updates and convergence guarantees. In particular, our method retains the same computational complexity of k-means and power k-means, but yields significant improvements over both. Its merits are thoroughly assessed on a suite of real and synthetic data experiments.