Clustering to minimize the sum of cluster diameters

  • Authors:
  • Moses Charikar;Rina Panigrahy

  • Affiliations:
  • Google Inc., Mountain View, CA and Computer Science Dept., Stanford University, Stanford, CA;Cisco Systems, San Jose, CA

  • Venue:
  • STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
  • Year:
  • 2001

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Abstract

We study the problem of clustering points in a metric space so as to minimize the sum of cluster diameters. Significantly improving on previous results, we present a primal-dual based constant factor approximation algorithm for this problem. We present a simple greedy algorithm that achieves a logarithmic approximation which also applies when the distance function is asymmetric. The previous best known result obtained a logarithmic approximation with a constant factor blowup in the number of clusters. We also obtain an incremental clustering algorithm that maintains a solution whose cost is at most a constant factor times that of optimal with a constant factor blowup in the number of clusters.