Title :
Contextual Sequential Pattern Mining
Author :
Rabatel, Julien ; Bringay, Sandra ; Poncelet, Pascal
Author_Institution :
Cap Omega, Tecnalia, Montpellier, France
Abstract :
Traditional sequential patterns do not take into account additional contextual information since patterns extracted from data are usually general. By considering the fact that a pattern is associated with one specific context the decision expert can then adapt his strategy considering the type of customers. In this paper we propose to mine more precise patterns of the form "young users buy products A and B then product C, while old users do not follow this same behavior". By highlighting relevant properties of such contexts, we show how contextual sequential patterns can be extracted by mining the database in a concise manner. We conduct our experimental evaluation on real-world data and demonstrate performance issues.
Keywords :
consumer behaviour; data mining; database management systems; pattern classification; contextual sequential pattern mining; customer behavior; database mining; decision expert; pattern extraction; Contextual Data; Sequential Patterns;
Conference_Titel :
Data Mining Workshops (ICDMW), 2010 IEEE International Conference on
Conference_Location :
Sydney, NSW
Print_ISBN :
978-1-4244-9244-2
Electronic_ISBN :
978-0-7695-4257-7
DOI :
10.1109/ICDMW.2010.182