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 : Abstract—Temporal and sequential data can be handled by 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 requests to the register form preceeds an increasing number of requests 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 extracting evolution patterns thanks to a relevant use of fuzzy sequential patterns and introduce our algorithms TED and EVA, designed for this task. Our proposal is validated through a set of experiments and a sample of extracted knowledge is discussed.
Type de document :
RR-08002, 2008
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Contributeur : Celine Fiot <>
Soumis le : jeudi 21 février 2008 - 09:53:46
Dernière modification le : samedi 27 janvier 2018 - 01:30:48


  • HAL Id : lirmm-00258079, version 1



Céline Fiot, Florent Masseglia, Anne Laurent, Maguelonne Teisseire. TED and EVA: Expressing Temporal Tendencies among Quantitative Variables using Fuzzy Sequential Patterns. RR-08002, 2008. 〈lirmm-00258079〉



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