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arXiv · 2002.07784

k-means++: few more steps yield constant approximation

Davin Choo·Christoph Grunau·Julian Portmann·Václav Rozhoň
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Abstract

The k-means++ algorithm of Arthur and Vassilvitskii (SODA 2007) is a state-of-the-art algorithm for solving the k-means clustering problem and is known to give an O(log k)-approximation in expectation. Recently, Lattanzi and Sohler (ICML 2019) proposed augmenting k-means++ with O(k log log k) local search steps to yield a constant approximation (in expectation) to the k-means clustering problem. In this paper, we improve their analysis to show that, for any arbitrarily small constant $\eps > 0$, with only $\eps k$ additional local search steps, one can achieve a constant approximation guarantee (with high probability in k), resolving an open problem in their paper.

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Davin Choo, Christoph Grunau, Julian Portmann, Václav Rozhoň. 2020-02-18. k-means++: few more steps yield constant approximation. https://arxiv.org/abs/2002.07784

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