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Poster

Scalable Algorithms for Individual Preference Stable Clustering

Ron Mosenzon · Ali Vakilian

MR1 & MR2 - Number 30
[ ]
Thu 2 May 8 a.m. PDT — 8:30 a.m. PDT

Abstract: In this paper, we study the individual preference (IP) stability, which is an notion capturing individual fairness and stability in clustering. Within this setting, a clustering is $\alpha$-IP stable when each data point's average distance to its cluster is no more than $\alpha$ times its average distance to any other cluster. In this paper, we study the natural local search algorithm for IP stable clustering. Our analysis confirms a $O(\log n)$-IP stability guarantee for this algorithm, where $n$ denotes the number of points in the input. Furthermore, by refining the local search approach, we show it runs in an almost linear time, $\tilde{O}(nk)$.

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