Coresets for k-Means and k-Median Clustering and their Applications
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In this paper, we show the existence of small coresets for the problems of computing k-median and k-means clustering for points in low dimension. In other words, we show that given a point set P in ^d, one can compute a weighted set ⊆ P, of size O(k ^-dn), such that one can compute the k-median/means clustering on instead of on P, and get an (1+)-approximation. As a result, we improve the fastest known algorithms for (1+)-approximate k-means and k-median clustering. Our algorithms have linear running time for a fixed k and . In addition, we can maintain the (1+)-approximate k-median or k-means clustering of a stream when points are being only inserted, using polylogarithmic space and update time.
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