Geometric clustering to minimize the sum of cluster sizes

  • Authors:
  • Vittorio Bilò;Ioannis Caragiannis;Christos Kaklamanis;Panagiotis Kanellopoulos

  • Affiliations:
  • Dipartimento di Matematica “Ennio De Giorgi”, Università di Lecce, Provinciale Lecce-Arnesano, Lecce, Italy;Research Academic Computer Technology Institute &, Department of Computer Engineering and Informatics, University of Patras, Rio, Greece;Research Academic Computer Technology Institute &, Department of Computer Engineering and Informatics, University of Patras, Rio, Greece;Research Academic Computer Technology Institute &, Department of Computer Engineering and Informatics, University of Patras, Rio, Greece

  • Venue:
  • ESA'05 Proceedings of the 13th annual European conference on Algorithms
  • Year:
  • 2005

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Abstract

We study geometric versions of the min-size k-clustering problem, a clustering problem which generalizes clustering to minimize the sum of cluster radii and has important applications. We prove that the problem can be solved in polynomial time when the points to be clustered are located on a line. For Euclidean spaces of higher dimensions, we show that the problem is NP-hard and present polynomial time approximation schemes. The latter result yields an improved approximation algorithm for the related problem of k-clustering to minimize the sum of cluster diameters.