Contextual Sequential Pattern Mining

Julien Rabatel 1, * Sandra Bringay 1, 2 Pascal Poncelet 1
* Auteur correspondant
1 TATOO - Fouille de données environnementales
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
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.
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Communication dans un congrès
DDDM: Domain Driven Data Mining, Dec 2010, Sydney, NSW, Australia. IEEE, Proceedings of the Domain Driven Data Mining Workshop (DDDM 2010) in conjunction with IEEE ICDM 2010, pp.981-988, 2010, 〈10.1109/ICDMW.2010.182〉
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Dernière modification le : jeudi 11 janvier 2018 - 06:26:17
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Julien Rabatel, Sandra Bringay, Pascal Poncelet. Contextual Sequential Pattern Mining. DDDM: Domain Driven Data Mining, Dec 2010, Sydney, NSW, Australia. IEEE, Proceedings of the Domain Driven Data Mining Workshop (DDDM 2010) in conjunction with IEEE ICDM 2010, pp.981-988, 2010, 〈10.1109/ICDMW.2010.182〉. 〈lirmm-00670950〉

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