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Let Ο΅>0 be any constant and let P be a set of n points in Rd. We design new streaming and approximation algorithms for clustering points of P. Consider the projective clustering problem: Given k,q<n, compute a set F of kq-flats such that the function fkqβ(P,Ο)=βpβPβd(p,F)Ο is minimized; here d(p,F) represents the distance of p to the closest q-flat in F. For Ο=β, we interpret fkqβ(P,Ο) to be maxrβPβd(r,F). When Ο=1,2 and β and q=0, the problem corresponds to the well-known k-median, k-mean and the k-center clustering problems respectively. Our two main technical contributions are as follows: (i) Consider an orthogonal projection of P to a randomly chosen O(CΟβ(q,Ο΅)logn/Ο΅2)-dimensional flat. For every subset SβP, we show that such a random projection will Ο΅-approximate f1qβ(S,Ο). This result holds for any integer norm Οβ₯1, including Ο=β; here CΟβ(q,Ο΅) is the size of the smallest coreset that Ο΅-approximates f1qβ(β ,Ο). For Ο=1,2 and β, CΟβ(q,Ο΅) is known to be a constant which depends only on q and Ο΅. (ii) We improve the size of the coreset when Ο=β. In particular, we improve the bounds of Cββ(q,Ο΅) to O(q3/Ο΅2) from the previously-known O(q6/Ο΅5log1/Ο΅). As applications, we obtain better approximation and streaming algorithms for various projective clustering problems over high dimensional point sets. E.g., when Ο=β and qβ₯1, we obtain a streaming algorithm that maintains an Ο΅-approximate solution using O((d+n)q3(logn/Ο΅4)) space, which is better than the input size O(nd)
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