Average Parameterization and Partial Kernelization for Computing Medians

  • Nadja Betzler
  • Jiong Guo
  • Christian Komusiewicz
  • Rolf Niedermeier
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6034)


We propose an effective polynomial-time preprocessing strategy for intractable median problems. Developing a new methodological framework, we show that if the input instances of generally intractable problems exhibit a sufficiently high degree of similarity between each other on average, then there are efficient exact solving algorithms. In other words, we show that the median problems Swap Median Permutation, Consensus Clustering, Kemeny Score, and Kemeny Tie Score all are fixed-parameter tractable with respect to the parameter “average distance between input objects”. To this end, we develop the new concept of “partial kernelization” and identify interesting polynomial-time solvable special cases for the considered problems.


Reduction Rule Consensus Cluster Preference List Input Instance Candidate Pair 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Nadja Betzler
    • 1
  • Jiong Guo
    • 2
  • Christian Komusiewicz
    • 1
  • Rolf Niedermeier
    • 1
  1. 1.Institut für InformatikFriedrich-Schiller-Universität JenaJenaGermany
  2. 2.Universität des SaarlandesSaarbrückenGermany

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