$t$-$k$-means: A Robust and Stable $k$-means Variant

TL;DR

The paper introduces t-k-means, a robust and stable variant of the classic k-means clustering algorithm, designed to handle heavy-tailed data and outliers more effectively while improving stability.

Contribution

It proposes a new t-k-means algorithm with theoretical analysis of robustness and stability, along with a fast version to enhance performance.

Findings

t-k-means outperforms standard k-means on heavy-tailed data

The method demonstrates improved stability with lower variance in results

Experiments confirm the effectiveness and efficiency of t-k-means

Abstract

$k$-means algorithm is one of the most classical clustering methods, which has been widely and successfully used in signal processing. However, due to the thin-tailed property of the Gaussian distribution, $k$-means algorithm suffers from relatively poor performance on the dataset containing heavy-tailed data or outliers. Besides, standard $k$-means algorithm also has relatively weak stability, $i.e.$ its results have a large variance, which reduces its credibility. In this paper, we propose a robust and stable $k$-means variant, dubbed the $t$-$k$-means, as well as its fast version to alleviate those problems. Theoretically, we derive the $t$-$k$-means and analyze its robustness and stability from the aspect of the loss function and the expression of the clustering center, respectively. Extensive experiments are also conducted, which verify the effectiveness and efficiency of the proposed method. The code for reproducing main results is available at \url{https://github.com/THUYimingLi/t-k-means}.