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• W2085751730 abstract "We present the first constant-factor approximation algorithm for the metric k -median problem. The k -median problem is one of the most well-studied clustering problems, i.e., those problems in which the aim is to partition a given set of points into clusters so that the points within a cluster are relatively close with respect to some measure. For the metric k -median problem, we are given n points in a metric space. We select k of these to be cluster centers and then assign each point to its closest selected center. If point j is assigned to a center i , the cost incurred is proportional to the distance between i and j . The goal is to select the k centers that minimize the sum of the assignment costs. We give a 6 2 3 -approximation algorithm for this problem. This improves upon the best previously known result of O (log k log log k ), which was obtained by refining and derandomizing a randomized O (log n log log n )-approximation algorithm of Bartal." @default.
• W2085751730 created "2016-06-24" @default.
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• W2085751730 date "2002-08-01" @default.
• W2085751730 modified "2023-10-18" @default.
• W2085751730 title "A Constant-Factor Approximation Algorithm for the k-Median Problem" @default.
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• W2085751730 doi "https://doi.org/10.1006/jcss.2002.1882" @default.
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