Clustering with Label Consistency

📝 Original Info

• Title: Clustering with Label Consistency
• ArXiv ID: 2512.19654
• Date: 2025-12-22
• Authors: Diptarka Chakraborty, Hendrik Fichtenberger, Bernhard Haeupler, Silvio Lattanzi, Ashkan Norouzi-Fard, Ola Svensson

📝 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 k-center and k-median problems.

📄 Full Content

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