Combining Constraint Programming and Constraint-Based Mining for Pattern Discovery

  • Mehdi Khiari
  • Patrice Boizumault
  • Bruno Crémilleux
Part of the Studies in Computational Intelligence book series (SCI, volume 398)


The large outputs of data mining methods hamper the individual and global analysis performed by the data analysts. That is why discovering patterns of higher level is an active research field. In this paper, by investigating the relationship between constraint-based mining and constraint satisfaction problems, we propose an approach to model and mine queries involving several local patterns (n-ary patterns). First, the user expresses his/her query under constraints involving n-ary patterns. Second, the constraints are formulated using constraint programming and solved by a constraint solver which generates the correct and complete set of solutions. This dissociation allows the user to express in a declarative way a large set of queries without taking care of their solving. Our approach also takes benefit from the recent progress on mining local patterns by pushing, with a solver on local patterns, all local constraints which can be inferred from the query. This approach enables us to model in a flexible way any set of constraints combining several local patterns and it leads to discover patterns of higher level. Experiments show the feasibility and the interest of our approach.


Local Pattern Constraint Satisfaction Problem Local Constraint Pattern Discovery Condensed Representation 
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 Berlin Heidelberg 2012

Authors and Affiliations

  • Mehdi Khiari
    • 1
  • Patrice Boizumault
    • 1
  • Bruno Crémilleux
    • 1
  1. 1.GREYC (CNRS - UMR 6072)Université de Caen Basse-NormandieCaen CedexFrance

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