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Mining Frequent Sequential Patterns under a Similarity Constraint

  • Matthieu Capelle
  • Cyrille Masson
  • Jean-François Boulicaut
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2412)

Abstract

Many practical applications are related to frequent sequential pattern mining, ranging from Web Usage Mining to Bioinformatics. To ensure an appropriate extraction cost for useful mining tasks, a key issue is to push the user-defined constraints deep inside the mining algorithms. In this paper, we study the search for frequent sequential patterns that are also similar to an user-defined reference pattern. While the effective processing of the frequency constraints is well-understood, our contribution concerns the identification of a relaxation of the similarity constraint into a convertible anti-monotone constraint. Both constraints are then used to prune the search space during a levelwise search. Preliminary experimental validations have confirmed the algorithm efficiency.

Keywords

Sequential Pattern Mining Algorithm Similarity Threshold Editing Operation Similarity 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 2002

Authors and Affiliations

  • Matthieu Capelle
    • 1
  • Cyrille Masson
    • 1
  • Jean-François Boulicaut
    • 1
  1. 1.Laboratoire d’Ingéniérie des Systèmes d’InformationInstitut National des Sciences Appliquées de LyonVilleurbanne CedexFrance

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