Contextual Sequential Pattern Mining

Julien Rabatel; Sandra Bringay; Pascal Poncelet

doi:10.1109/ICDMW.2010.182

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Contextual Sequential Pattern Mining

Julien Rabatel, Sandra Bringay et Pascal Poncelet

Proceedings of the Domain Driven Data Mining Workshop (DDDM 2010) in conjunction with IEEE ICDM 2010, pp.981-988

DDDM: Domain Driven Data Mining (Sydney, NSW, Australia, 13/12/2010)

12/2010

DOI: https://doi.org/10.1109/ICDMW.2010.182

Résumé

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.

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Détails

Titre: Contextual Sequential Pattern Mining
Créateurs - sans rôle: Julien Rabatel - Centre National de la Recherche Scientifique
Sandra Bringay - Université de Montpellier
Pascal Poncelet - Université de Montpellier
Détails de publication: Proceedings of the Domain Driven Data Mining Workshop (DDDM 2010) in conjunction with IEEE ICDM 2010, pp.981-988
Colloque: DDDM: Domain Driven Data Mining (Sydney, NSW, Australia, 13/12/2010)
Identifiants: 9936421609311
Unité académique: Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier - LIRMM
Langue: English
Type de ressource: Conference proceeding
Champs locaux: lirmm-00670950