TED and EVA : Expressing Temporal Tendencies Among Quantitative Variables Using Fuzzy Sequential Patterns

Céline Fiot 1 Florent Masseglia 1 Anne Laurent 2 Maguelonne Teisseire 2
1 AxIS - Usage-centered design, analysis and improvement of information systems
CRISAM - Inria Sophia Antipolis - Méditerranée , Inria Paris-Rocquencourt
2 TATOO - Fouille de données environnementales
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Temporal data can be handled in many ways for discovering specific knowledge. Sequential pattern mining is one of these relevant approaches when dealing with temporally annotated data. It allows discovering frequent sequences embedded in the records. In the access data of a commercial Web site, one may, for instance, discover that "5% of the users request the page register.php 3 times and then request the page help.html". However, symbolic or fuzzy sequential patterns, in their current form, do not allow extracting temporal tendencies that are typical of sequential data. By means of temporal tendency mining, one may discover in the same access data that "an increasing number of accesses to the register form preceeds an increasing number of accesses to the help page a few seconds later". It would be easy to conclude that the users either quickly succeed in registering or make several attempts before they look at the help page within a few seconds. In this paper, we propose the definition of evolution patterns that allow discovering such knowledge. We show how to extract evolution patterns thanks to fuzzy sequential pattern mining techniques. We introduce our algorithms Ted and Eva, designed for evolution pattern mining. Our proposal is validated by experiments and a sample of extracted knowledge is discussed.
Type de document :
Communication dans un congrès
WCCI408: IEEE World Congress on Computational Intelligence (Fuzz-IEEE'08: IEEE International Conference on Fuzzy Sets and Systems), France. pp.8, 2008, 〈http://www.wcci2008.org〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00273907
Contributeur : Celine Fiot <>
Soumis le : mercredi 16 avril 2008 - 16:22:37
Dernière modification le : vendredi 25 mai 2018 - 12:02:04

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  • HAL Id : lirmm-00273907, version 1

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Céline Fiot, Florent Masseglia, Anne Laurent, Maguelonne Teisseire. TED and EVA : Expressing Temporal Tendencies Among Quantitative Variables Using Fuzzy Sequential Patterns. WCCI408: IEEE World Congress on Computational Intelligence (Fuzz-IEEE'08: IEEE International Conference on Fuzzy Sets and Systems), France. pp.8, 2008, 〈http://www.wcci2008.org〉. 〈lirmm-00273907〉

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