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Min Sum Clustering with Penalties

  • Refael Hassin
  • Einat Or
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3669)

Abstract

Traditionally, clustering problems are investigated under the assumption that all objects must be clustered. A shortcoming of this formulation is that a few distant objects, called outliers, may exert a disproportionately strong influence over the solution. In this work we investigate the k -min-sum clustering problem while addressing outliers in a meaningful way.

Given a complete graph G = (V,E), a weight function w : EIN 0 on its edges, and \(p \rightarrow {\it {IN}_{o}}\) a penalty function on its nodes, the penalized k -min-sum problem is the problem of finding a partition of V to k+1 sets, {S 1,...,S k + 1}, minimizing \(\sum_{i=1}^{k}\) w(S i )+p(S k + 1), where for S ⊆ V w(S) = \(\sum_{e=\{{\it i},{\it j}\} \subset {\it S}}\) w e , and p(S) = \(\sum_{i \in S}{^p_i}\).

We offer an efficient 2-approximation to the penalized 1-min-sum problem using a primal-dual algorithm. We prove that the penalized 1-min-sum problem is NP-hard even if w is a metric and present a randomized approximation scheme for it. For the metric penalized k-min-sum problem we offer a 2-approximation.

Keywords

Complete Graph Exhaustive Search Cluster Problem Facility Location Problem Maximal Solution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Refael Hassin
    • 1
  • Einat Or
    • 1
  1. 1.Department of Statistics and Operations ResearchTel Aviv UniversityTel AvivIsrael

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