Fixed-Parameter Algorithms for Graph-Modeled Date Clustering

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


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.


Search Tree Consensus Cluster Dense Subgraph Search Tree Algorithm Problem Kernel 
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 2009

Authors and Affiliations

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

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