Softening the Blow of Frequent Sequence Analysis: Soft Constraints and Temporal Accuracy

Abstract : Mining temporal knowledge has many applications. Such knowledge can be all the more interesting as some time constraints between events can be integrated during the mining task. Both in data mining and machine learning, some methods have been proposed to extract and manage such knowledge using temporal constraints. 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. Within this context, we propose an approach based on sequence graphs derived from soft temporal constraints. These relaxed constraints enable us to find more generalized sequential patterns. We also propose a temporal accuracy measure to provide the user with a tool for analysing the numerous extracted patterns.
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International Journal of Web Engineering and Technology, Inderscience, 2009, Web-based Knowledge Representation and Management, pp.20
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00196964
Contributeur : Celine Fiot <>
Soumis le : vendredi 14 décembre 2007 - 08:30:50
Dernière modification le : jeudi 24 mai 2018 - 15:59:23

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  • HAL Id : lirmm-00196964, version 1

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Céline Fiot, Anne Laurent, Maguelonne Teisseire. Softening the Blow of Frequent Sequence Analysis: Soft Constraints and Temporal Accuracy. International Journal of Web Engineering and Technology, Inderscience, 2009, Web-based Knowledge Representation and Management, pp.20. 〈lirmm-00196964〉

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