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Approximation Algorithms for Clustering to Minimize the Sum of Diameters
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Approximation Algorithms for Clustering to Minimize the Sum of Diameters
Srinivas R. Doddi4 , Madhav V. Marathe5 , S. S. Ravi6 , David Scot Taylor7 and Peter Widmayer8 
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Los Alamos National Laboratory, P. O. Box 1663, MS B265, Los Alamos, NM 87545, USA |
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Los Alamos National Laboratory, P. O. Box 1663, MS M997, Los Alamos, NM 87545, USA |
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Department of Computer Science, University at Albany - State University of New York, Albany, NY 12222, USA |
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Department of Computer Science, University of California, Los Angeles, CA 90095-1596, USA |
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Institute for Theoretical Computer Science ETH, 8092 Zürich, Switzerland |
Abstract
We consider the problem of partitioning the nodes of a complete edge weighted graph into k clusters so as to minimize the sum of the diameters of the clusters. Since the problem is NP-complete, our focus is on the
development of good approximation algorithms. When edge weights satisfy the triangle inequality, we present the first approximation
algorithm for the problem. The approximation algorithm yields a solution that has no more than 10k clusters such that the
total diameter of these clusters is within a factor O(log (n/k)) of the optimal value for k clusters, where n is the number of nodes in the complete graph. For any fixed k, we present an approximation algorithm that produces k clusters whose total diameter is at most twice the optimal value. When the distances are not required to satisfy the triangle
inequality, we show that, unless P = NP, for any n > 1, there is no polynomial time approximation algorithm that can provide a performance guarantee of n even when the number of clusters is fixed at 3. Other results obtained include a polynomial time algorithm for the problem
when the underlying graph is a tree with edge weights.
Research Supported by Department of Energy Contract W-7405-ENG-36 and by NSF Grant CCR-97-34936.
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