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.
Document type :
Conference papers
Complete list of metadatas

https://hal-lirmm.ccsd.cnrs.fr/lirmm-00106087
Contributor : Christine Carvalho de Matos <>
Submitted on : Friday, October 13, 2006 - 10:23:08 AM
Last modification on : Wednesday, November 6, 2019 - 1:22:53 AM

Links full text

Identifiers

Citation

Marc Plantevit, Yeow Wei Choong, Anne Laurent, Dominique Laurent, Maguelonne Teisseire. M2SP: Mining Sequential Patterns Among Several Dimensions. PKDD: Knowledge Discovery in Databases, Oct 2005, Porto, Portugal. pp.205-216, ⟨10.1007/11564126_23⟩. ⟨lirmm-00106087⟩

Share

Metrics

Record views

170