Efficient Mining of Sequential Patterns with Time Constraints: Reducing the Combinations

Florent Masseglia 1 Pascal Poncelet 2, 3 Maguelonne Teisseire 3
1 AxIS - Usage-centered design, analysis and improvement of information systems
CRISAM - Inria Sophia Antipolis - Méditerranée , Inria Paris-Rocquencourt
3 TATOO - Fouille de données environnementales
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : In this paper we consider the problem of discovering sequential patterns by handling time constraints as defined in the Gsp algorithm. While sequential patterns could be seen as temporal relationships between facts embedded in the database where considered facts are merely characteristics of individuals or observations of individual behavior, generalized sequential patterns aim to provide the end user with a more flexible handling of the transactions embedded in the database. We thus propose a new efficient algorithm, called Gtc (Graph for Time Constraints) for mining such patterns in very large databases. It is based on the idea that handling time constraints in the earlier stage of the data mining process can be highly beneficial. One of the most significant new feature of our approach is that handling of time constraints can be easily taken into account in traditional levelwise approaches since it is carried out prior to and separately from the counting step of a data sequence. Our test shows that the proposed algorithm performs significantly faster than a state-of-the-art sequence mining algorithm.
Type de document :
Article dans une revue
Expert Systems with Applications, Elsevier, 2009, 36 (2), pp.2677-2690. 〈10.1016/j.eswa.2008.01.021〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00272632
Contributeur : Maguelonne Teisseire <>
Soumis le : vendredi 11 avril 2008 - 16:20:00
Dernière modification le : vendredi 25 mai 2018 - 12:02:04

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Florent Masseglia, Pascal Poncelet, Maguelonne Teisseire. Efficient Mining of Sequential Patterns with Time Constraints: Reducing the Combinations. Expert Systems with Applications, Elsevier, 2009, 36 (2), pp.2677-2690. 〈10.1016/j.eswa.2008.01.021〉. 〈lirmm-00272632〉

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