Approximating min-sum k-clustering in metric spaces

  • Authors:
  • Yair Bartal;Moses Charikar;Danny Raz

  • Affiliations:
  • Computer Science Dept., Hebrew University, Israel and Bell Labs, Lucent Technologies;Google, Inc., Mountain View, CA and Computer Science Dept., Stanford University;Computer Science Dept., Technion, Israel and Bell Labs, Lucent Technologies

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

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Abstract

The min-sum k-clustering problem in a metric space is to find a partition of the space into k clusters as to minimize the total sum of distances between pairs of points assigned to the same cluster. We give the first polynomial time non-trivial approximation algorithm for this problem. The algorithm provides an $\ratio$ approximation to the min-sum k-clustering problem in general metric spaces, with running time $\runtime$. The result is based on embedding of metric spaces into hierarchically separated trees. We also provide a bicriteria approximation result that provides a constant approximation factor solution with only a constant factor increase in the number of clusters. This result is obtained by modifying and drawing ideas from recently developed primal dual approximation algorithms for facility location.