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Book Sections Year : 2013

Mining Sequential Patterns: a Context-Aware Approach


Traditional sequential patterns do not take into account contextual infor- mation associated with sequential data. For instance, when studying purchases of customers in a shop, a sequential pattern could be "frequently, customers buy prod- ucts A and B at the same time, and then buy product C". Such a pattern does not con- sider 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 correspond- ing 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 effi- cient extraction algorithm. We conduct our experimental evaluation on real-world data and demonstrate performance issues.
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lirmm-00732659 , version 1 (16-09-2012)


  • HAL Id : lirmm-00732659 , version 1


Julien Rabatel, Sandra Bringay, Pascal Poncelet. Mining Sequential Patterns: a Context-Aware Approach. Springer. Advances in Knowledge Discovery and Management, 3, pp.23-41, 2013. ⟨lirmm-00732659⟩
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