Fixed-Parameter Algorithms for Graph-Modeled Date Clustering

  • Jiong Guo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5532)

Abstract

We survey some practical techniques for designing fixed-parameter algorithms for NP-hard graph-modeled data clustering problems. Such clustering problems ask to modify a given graph into a union of dense subgraphs. In particular, we discuss (polynomial-time) kernelizations and depth-bounded search trees and provide concrete applications of these techniques. After that, we shortly review the use of two further algorithmic techniques, iterative compression and average parameterization, applied to graph-modeled data clustering. Finally, we address some challenges for future research.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jiong Guo
    • 1
  1. 1.Institut für InformatikFriedrich-Schiller-Universität JenaJenaGermany

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