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Combining CSP and Constraint-Based Mining for Pattern Discovery

  • Mehdi Khiari
  • Patrice Boizumault
  • Bruno Crémilleux
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6017)

Abstract

A well-known limitation of a lot of data mining methods is the huge number of patterns which are discovered: these large outputs hamper the individual and global analysis performed by the end-users of data. That is why discovering patterns of higher level is an active research field. In this paper, we investigate the relationship between local constraint-based mining and constraint satisfaction problems and we propose an approach to model and mine patterns combining several local patterns, i.e., patterns defined by n-ary constraints. The user specifies a set of n-ary constraints and a constraint solver generates the whole set of solutions. Our approach 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 n-ary ones. This approach enables us to model in a flexible way any set of constraints combining several local patterns. Experiments show the feasibility of our approach.

Keywords

Local Pattern Constraint Programming Mine Pattern Constraint Satisfaction Problem Local 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 2010

Authors and Affiliations

  • Mehdi Khiari
    • 1
  • Patrice Boizumault
    • 1
  • Bruno Crémilleux
    • 1
  1. 1.Campus Côte de NacreGREYC, Université de Caen Basse-NormandieCaen CedexFrance

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