Diptarka Chakraborty, National University of Singapore

Abstract

Designing efficient, effective, and consistent metric clustering algorithms is a significant challenge that is attracting growing attention. Traditional approaches focus on the stability of cluster centers. Unfortunately, this neglects the real-world need for stable point labels. 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 algorithms for the classic k-center and k-median problems.