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Contextual Sequential Pattern Mining

Authors :
Pascal Poncelet
Julien Rabatel
Sandra Bringay
Fouille de données environnementales (TATOO)
Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM)
Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)
Université Paul-Valéry - Montpellier 3 (UPVM)
Source :
ICDM Workshops, Proceedings of the Domain Driven Data Mining Workshop (DDDM 2010) in conjunction with IEEE ICDM 2010, DDDM: Domain Driven Data Mining, DDDM: Domain Driven Data Mining, Dec 2010, Sydney, NSW, Australia. pp.981-988, ⟨10.1109/ICDMW.2010.182⟩
Publication Year :
2010
Publisher :
IEEE, 2010.

Abstract

International audience; 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.

Details

Database :
OpenAIRE
Journal :
2010 IEEE International Conference on Data Mining Workshops
Accession number :
edsair.doi.dedup.....d0f10916b19f4bfce65e76553d9ac627
Full Text :
https://doi.org/10.1109/icdmw.2010.182