Journal of Intelligent Information Systems

, Volume 44, Issue 2, pp 193–221 | Cite as

Soft constraints for pattern mining

  • Willy UgarteEmail author
  • Patrice Boizumault
  • Samir Loudni
  • Bruno Crémilleux
  • Alban Lepailleur


Constraint-based pattern discovery is at the core of numerous data mining tasks. Patterns are extracted with respect to a given set of constraints (frequency, closedness, size, etc). In practice, many constraints require threshold values whose choice is often arbitrary. This difficulty is even harder when several thresholds are required and have to be combined. Moreover, patterns barely missing a threshold will not be extracted even if they may be relevant. The paper advocates the introduction of softness into the pattern discovery process. By using Constraint Programming, we propose efficient methods to relax threshold constraints as well as constraints involved in patterns such as the top-k patterns and the skypatterns. We show the relevance and the efficiency of our approach through a case study in chemoinformatics for discovering toxicophores.


Constraint-based pattern mining Soft constraints Soft skypatterns Constraint Programming Disjonctive relaxation Chemoinformatics 


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Willy Ugarte
    • 1
    Email author
  • 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 CaenCaen CedexFrance

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