M2SP: Mining Sequential Patterns Among Several Dimensions

Abstract : Mining sequential patterns aims at discovering correlations between events through time. However, even if many works have dealt with sequential pattern mining, none of them considers frequent sequential patterns involving several dimensions in the general case. In this paper, we propose a novel approach, called M 2 SP, to mine multidimensional sequential patterns. The main originality of our proposition is that we obtain not only intra-pattern sequences but also inter-pattern sequences. Moreover, we consider generalized multidimensional sequential patterns, called jokerized patterns, in which some of the dimension values may not be instanciated. Experiments on synthetic data are reported and show the scalability of our approach.
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Communication dans un congrès
A. Jorge. PKDD: Knowledge Discovery in Databases, Oct 2005, Porto, Portugal. Springer-Verlag, European Conference on Principles of Data Mining and Knowledge Discovery, LNS (3721), pp.205-216, 2005, 〈10.1007/11564126_23〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00106087
Contributeur : Christine Carvalho de Matos <>
Soumis le : vendredi 13 octobre 2006 - 10:23:08
Dernière modification le : jeudi 22 novembre 2018 - 01:10:04

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Marc Plantevit, Yeow Wei Choong, Anne Laurent, Dominique Laurent, Maguelonne Teisseire. M2SP: Mining Sequential Patterns Among Several Dimensions. A. Jorge. PKDD: Knowledge Discovery in Databases, Oct 2005, Porto, Portugal. Springer-Verlag, European Conference on Principles of Data Mining and Knowledge Discovery, LNS (3721), pp.205-216, 2005, 〈10.1007/11564126_23〉. 〈lirmm-00106087〉

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