Constrained Clustering Using SAT

  • Jean-Philippe Métivier
  • Patrice Boizumault
  • Bruno Crémilleux
  • Mehdi Khiari
  • Samir Loudni
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7619)


Constrained clustering - finding clusters that satisfy user-specified constraints - aims at providing more relevant clusters by adding constraints enforcing required properties. Leveraging the recent progress in declarative and constraint-based pattern mining, we propose an effective constraint-clustering approach handling a large set of constraints which are described by a generic constraint-based language. Starting from an initial solution, queries can easily be refined in order to focus on more interesting clustering solutions. We show how each constraint (and query) is encoded in SAT and solved by taking benefit from several features of SAT solvers. Experiments performed using MiniSat on several datasets from the UCI repository show the feasibility and the advantages of our approach.


Unit Propagation Constraint Programming Pattern Mining Conjunctive Normal Form Cardinality Constraint 
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 2012

Authors and Affiliations

  • Jean-Philippe Métivier
    • 1
  • Patrice Boizumault
    • 1
  • Bruno Crémilleux
    • 1
  • Mehdi Khiari
    • 1
  • Samir Loudni
    • 1
  1. 1.University of Caen Basse-Normandie – GREYC (CNRS UMR 6072)CaenFrance

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