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Redondo Beach, California

Nov. 12, 2000 to Nov. 14, 2000

ISBN: 0-7695-0850-2

pp: 349

R. Ostrovsky , Telcordia Technol., Morristown, NJ, USA

Y. Rabani , Telcordia Technol., Morristown, NJ, USA

ABSTRACT

We deal with the problem of clustering data points. Given n points in a larger set (for example, R/sup d/) endowed with a distance function (for example, L/sup 2/ distance), we would like to partition the data set into k disjoint clusters, each with a "cluster center", so as to minimize the sum over all data points of the distance between the point and the center of the cluster containing the point. The problem is provably NP-hard in some high dimensional geometric settings, even for k=2. We give polynomial time approximation schemes for this problem in several settings, including the binary cube (0, 1)/sup d/ with Hamming distance, and R/sup d/ either with L/sup 1/ distance, or with L/sup 2/ distance, or with the square of L/sup 2/ distance. In all these settings, the best previous results were constant factor approximation guarantees. We note that our problem is similar in flavor to the k-median problem (and the related facility location problem), which has been considered in graph-theoretic and fixed dimensional geometric settings, where it becomes hard when k is part of the input. In contrast, we study the problem when k is fixed, but the dimension is part of the input. Our algorithms are based on a dimension reduction construction for the Hamming cube, which may be of independent interest.

INDEX TERMS

computational complexity; computational geometry; pattern clustering; polynomial time approximation schemes; geometric k-clustering; data point clustering; distance function; data set partitioning; NP-hard problem; high dimensional geometry; binary cube; Hamming distance; k-median problem

CITATION

R. Ostrovsky,
Y. Rabani,
"Polynomial time approximation schemes for geometric k-clustering",

*FOCS*, 2000, 2013 IEEE 54th Annual Symposium on Foundations of Computer Science, 2013 IEEE 54th Annual Symposium on Foundations of Computer Science 2000, pp. 349, doi:10.1109/SFCS.2000.892123