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Mining sequential patterns: a context-aware approach.
| Content Provider | CiteSeerX |
|---|---|
| Author | Rabatel, Julien Bringay, Ra Poncelet, Pascal Bringay, Sandra |
| Abstract | 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 contextdependent 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. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Sequential Pattern Context-aware Approach Account Contextual Information Young Customer Form Buying Product Contextdependent Sequential Pattern Buying Product Efficient Extraction Algorithm Demonstrate Performance Issue Context-dependent Sequential Pattern Context Corresponding Socio-professional Category Abstract Traditional Sequential Pattern Sequential Data Real-world Data General Behavior Experimental Evaluation Decision Expert Highlight Relevant Property |
| Content Type | Text |
