Clustering with Label Consistency
Data Structures and Algorithms
2025-12-23 v1 Artificial Intelligence
Abstract
Designing efficient, effective, and consistent metric clustering algorithms is a significant challenge attracting growing attention. Traditional approaches focus on the stability of cluster centers; unfortunately, this neglects the real-world need for stable point labels, i.e., stable assignments of points to named sets (clusters). In this paper, we address this gap by initiating the study of label-consistent metric clustering. We first introduce a new notion of consistency, measuring the label distance between two consecutive solutions. Then, armed with this new definition, we design new consistent approximation algorithms for the classical -center and -median problems.
Cite
@article{arxiv.2512.19654,
title = {Clustering with Label Consistency},
author = {Diptarka Chakraborty and Hendrik Fichtenberger and Bernhard Haeupler and Silvio Lattanzi and Ashkan Norouzi-Fard and Ola Svensson},
journal= {arXiv preprint arXiv:2512.19654},
year = {2025}
}