Extended Time Constraints for Sequence Mining

Abstract : Many applications require techniques for temporal knowledge discovery. Some of those approaches can handle time constraints between events. In particular some work has been done to mine generalized sequential patterns. However, such constraints are often too crisp or need a very precise assessment to avoid erroneous information. Therefore, in this paper we propose to soften temporal constraints used for generalized sequential pattern mining. To handle these constraints while data mining, we design an algorithm based on sequence graphs. Moreover, as these relaxed constraints may extract more generalized patterns, we propose temporal accuracy measure for helping the analysis of the numerous discovered patterns.
Type de document :
Communication dans un congrès
TIME'07: 14th IEEE International Symposium on Temporal Representation and Reasoning, Jun 2007, Alicante, Spain, pp.105-116, 2007
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00160536
Contributeur : Celine Fiot <>
Soumis le : vendredi 6 juillet 2007 - 11:22:30
Dernière modification le : jeudi 24 mai 2018 - 15:59:23

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  • HAL Id : lirmm-00160536, version 1

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Céline Fiot, Anne Laurent, Maguelonne Teisseire. Extended Time Constraints for Sequence Mining. TIME'07: 14th IEEE International Symposium on Temporal Representation and Reasoning, Jun 2007, Alicante, Spain, pp.105-116, 2007. 〈lirmm-00160536〉

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