mlsanityData to 14 Oct 2026

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

arXiv 2002.07784 ↗First posted 18 Feb 2020cs.DS cs.LGcited by 16

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Abstract (arXiv)

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.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Feb 2020.

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Citing papers per year: 2021: 1; 2022: 3; 2023: 3; 2024: 3; 2025: 4; 2026: 2.

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