Mining (Soft-) Skypatterns Using Constraint Programming

  • Willy Ugarte
  • Patrice Boizumault
  • Samir Loudni
  • Bruno Crémilleux
  • Alban Lepailleur
Part of the Studies in Computational Intelligence book series (SCI, volume 615)


Within the pattern mining area, skypatterns enable to express a user-preference point of view according to a dominance relation. In this paper, we deal with the introduction of softness in the skypattern mining problem. First, we show how softness can provide convenient patterns that would be missed otherwise. Then, thanks to Constraint Programming, we propose a generic and efficient method to mine skypatterns as well as soft ones. Finally, we show the relevance and the effectiveness of our approach through experiments on UCI benchmarks and a case study in chemoinformatics for discovering toxicophores.


Constraint Programming Pattern Mining Pareto Frontier Skyline Query Skyline Point 
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.



This work is partly supported by the ANR (French Research National Agency) funded project FiCOLOFO ANR-10-BLA-0214. The authors would like to thank Arnaud Soulet (University François Rabelais of Tours, France), for providing the Aetheris program and his highly valuable comments.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Willy Ugarte
    • 1
  • Patrice Boizumault
    • 1
  • Samir Loudni
    • 1
  • Bruno Crémilleux
    • 1
  • Alban Lepailleur
    • 2
  1. 1.GREYC (CNRS UMR 6072) – University of CaenCaenFrance
  2. 2.CERMN (UPRES EA 4258 - FR CNRS 3038 INC3M) – University of Caen Boulevard BecquerelCaen CedexFrance

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