Proceedings of the twenty-fourth annual symposium on Computational geometry
Property testing
Property testing
A unified framework for approximating and clustering data
Proceedings of the forty-third annual ACM symposium on Theory of computing
Deterministic sublinear-time approximations for metric 1-median selection
Information Processing Letters
Learning Big (Image) Data via Coresets for Dictionaries
Journal of Mathematical Imaging and Vision
Streaming with minimum space: An algorithm for covering by two congruent balls
Theoretical Computer Science
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In this paper we show that there exists a $(k,\varepsilon)$-coreset for k-median and k-means clustering of n points in ${\cal R}^d,$ which is of size independent of n. In particular, we construct a $(k,\varepsilon)$-coreset of size $O(k^2/\varepsilon^d)$ for k-median clustering, and of size $O(k^3/\varepsilon^{d+1})$ for k-means clustering.