k-sums: another side of k-means

TL;DR

This paper introduces a stochastic reallocation method for k-means clustering that converges faster and finds better local minima, along with a new intra-cluster distance minimization target, outperforming existing variants.

Contribution

It presents a novel stochastic optimization approach for k-means and introduces a new intra-cluster distance minimization model, enhancing clustering performance.

Findings

Faster convergence to better local minima.

Outperforms traditional k-means and variants on multiple datasets.

Introduces a new intra-cluster distance minimization target.

Abstract

In this paper, the decades-old clustering method k-means is revisited. The original distortion minimization model of k-means is addressed by a pure stochastic minimization procedure. In each step of the iteration, one sample is tentatively reallocated from one cluster to another. It is moved to another cluster as long as the reallocation allows the sample to be closer to the new centroid. This optimization procedure converges faster to a better local minimum over k-means and many of its variants. This fundamental modification over the k-means loop leads to the redefinition of a family of k-means variants. Moreover, a new target function that minimizes the summation of pairwise distances within clusters is presented. We show that it could be solved under the same stochastic optimization procedure. This minimization procedure built upon two minimization models outperforms k-means and its variants considerably with different settings and on different datasets.