Recognizing Unexpected Recurrence Behaviors with Fuzzy Methods in Sequence Databases

Abstract : The recognition of unexpected behaviors in databases is an important problem in many real-world applications. In the previous studies, the unexpectedness is mainly stated within the context of the most-studied patterns, association rules, or sequential patterns. In this paper, we first propose the notion of fuzzy recurrence rule, a new kind of rule-based behavior in sequence databases, and then we introduce the problem of recognizing unexpected sequences contradicting the beliefs on fuzzy recurrence rules, with fuzzy measures. We also develop, UFR, an algorithm for discovering unexpected recurrence behaviors in a sequence database. Our approach is evaluated with Web access log data.
Type de document :
Communication dans un congrès
CSTST'08: International Conference on Soft Computing as Transdisciplinary Science and Technology, pp.37-43, 2008
Liste complète des métadonnées

Littérature citée [28 références]  Voir  Masquer  Télécharger

https://hal-lirmm.ccsd.cnrs.fr/lirmm-00324590
Contributeur : Haoyuan Li <>
Soumis le : mardi 24 mars 2009 - 11:01:33
Dernière modification le : jeudi 24 mai 2018 - 15:59:22
Document(s) archivé(s) le : vendredi 4 juin 2010 - 11:47:42

Fichier

llp08cstst.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : lirmm-00324590, version 1

Collections

Citation

Haoyuan Li, Anne Laurent, Pascal Poncelet. Recognizing Unexpected Recurrence Behaviors with Fuzzy Methods in Sequence Databases. CSTST'08: International Conference on Soft Computing as Transdisciplinary Science and Technology, pp.37-43, 2008. 〈lirmm-00324590〉

Partager

Métriques

Consultations de la notice

112

Téléchargements de fichiers

123