Mining Sequential Patterns: A Context-Aware Approach

  • Julien Rabatel
  • Sandra Bringay
  • Pascal Poncelet
Part of the Studies in Computational Intelligence book series (SCI, volume 471)


Traditional sequential patterns do not take into account contextual information associated with sequential data. For instance, when studying purchases of customers in a shop, a sequential pattern could be “frequently, customers buy products A and B at the same time, and then buy product C”. Such a pattern does not consider the age, the gender or the socio-professional category of customers. However, by taking into account contextual information, a decision expert can adapt his/her strategy according to the type of customers. In this paper, we focus on the analysis of a given context (e.g., a category of customers) by extracting context-dependent sequential patterns within this context. For instance, given the context corresponding to young customers, we propose to mine patterns of the form “buying products A and B then product C is a general behavior in this population” or “buying products B and D is frequent for young customers only”. We formally define such context-dependent sequential patterns and highlight relevant properties that lead to an efficient extraction algorithm. We conduct our experimental evaluation on real-world data and demonstrate performance issues.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Julien Rabatel
    • 1
    • 2
  • Sandra Bringay
    • 2
    • 3
  • Pascal Poncelet
    • 2
  1. 1.Tecnalia, Cap OmegaMontpellier Cedex 2France
  2. 2.LIRMM (CNRS UMR 5506), Univ. Montpellier 2Montpellier Cedex 5France
  3. 3.Dpt. MIAPUniv. Montpellier 3Montpellier Cedex 5France

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