Mining Sequential Patterns: a Context-Aware Approach

Julien Rabatel 1 Sandra Bringay 1, 2 Pascal Poncelet 1
1 ADVANSE - ADVanced Analytics for data SciencE
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : 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.
Mots-clés : Contextual Patterns
Type de document :
Chapitre d'ouvrage
Springer. Advances in Knowledge Discovery and Management, 3, pp.23-41, 2013
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Soumis le : dimanche 16 septembre 2012 - 02:11:24
Dernière modification le : jeudi 24 mai 2018 - 15:59:25
Document(s) archivé(s) le : lundi 17 décembre 2012 - 02:35:13


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  • 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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