A Comparative Study of Local Search Algorithms for Correlation Clustering

  • Evgeny Levinkov
  • Alexander Kirillov
  • Bjoern Andres
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10496)


This paper empirically compares four local search algorithms for correlation clustering by applying these to a variety of instances of the correlation clustering problem for the tasks of image segmentation, hand-written digit classification and social network analysis. Although the local search algorithms establish neither lower bounds nor approximation certificates, they converge monotonously to a fixpoint, offering a feasible solution at any time. For some algorithms, the time of convergence is affordable for all instances we consider. This finding encourages a broader application of correlation clustering, especially in settings where the number of clusters is not known and needs to be estimated from data.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Evgeny Levinkov
    • 1
  • Alexander Kirillov
    • 2
  • Bjoern Andres
    • 1
  1. 1.Max Planck Institute for Informatics, Saarland Informatics CampusSaarbrückenGermany
  2. 2.Computer Vision LabTechnische Universität DresdenDresdenGermany

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