Mining Sequential Patterns: A Context-Aware Approach

Abstract

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.