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arXiv · 2507.11260

Towards Tight Robust Coresets for $k$-Medians Clustering

Lingxiao Huang·Zhenyu Jiang·Yi Li·Xuan Wu
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Abstract

This paper considers coresets for the robust $k$-medians problem with $m$ outliers, and new constructions in various metric spaces are obtained. Specifically, for metric spaces with a bounded VC or doubling dimension $d$, the coreset size is $O(m) + \tilde{O}(kd\varepsilon^{-2})$, which is optimal up to logarithmic factors. For Euclidean spaces, the coreset size is $O(m\varepsilon^{-1}) + \tilde{O}(\min\{k^{4/3}\varepsilon^{-2}, k\varepsilon^{-3}\})$, improving upon a recent result by Jiang and Lou (ICALP 2025). These results also extend to robust $(k,z)$-clustering, yielding, for VC and doubling dimension, a coreset size of $O(m) + \tilde{O}(kd\varepsilon^{-2z})$ with the optimal linear dependence on $m$. This extended result improves upon the earlier work of Huang et al. (SODA 2025). The techniques introduce novel dataset decompositions, enabling chaining arguments to be applied jointly across multiple components.

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Lingxiao Huang, Zhenyu Jiang, Yi Li, Xuan Wu. 2025-07-15. Towards Tight Robust Coresets for $k$-Medians Clustering. https://arxiv.org/abs/2507.11260

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