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Improved Guarantees for k-means++ and k-means++ Parallel

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arxiv 2010.14487 v1 pith:NLH7HOMO submitted 2020-10-27 cs.LG cs.DS

Konstantin Makarychev , Aravind Reddy , Liren Shan This is my paper · ORCID
classification cs.LGcs.DS
keywords k-meansparallelapproximationguaranteesalgorithmsimprovedalgorithmanalyses
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In this paper, we study k-means++ and k-means++ parallel, the two most popular algorithms for the classic k-means clustering problem. We provide novel analyses and show improved approximation and bi-criteria approximation guarantees for k-means++ and k-means++ parallel. Our results give a better theoretical justification for why these algorithms perform extremely well in practice. We also propose a new variant of k-means++ parallel algorithm (Exponential Race k-means++) that has the same approximation guarantees as k-means++.

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