Discovering Fuzzy Unexpected Sequences with Concept Hierarchies - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier
Article Dans Une Revue International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems Année : 2009

Discovering Fuzzy Unexpected Sequences with Concept Hierarchies

Résumé

Sequential pattern mining is the method that has received much attention in sequence data mining research and applications, however, a drawback is that it does not profit from prior knowledge of domains. In our previous work, we proposed a belief-driven method with fuzzy set theory for discovering the unexpected sequences that contradict existing knowledge of data, including occurrence constraints and semantic contradictions. In this paper, we present a new approach that discovers unexpected sequences with determining semantic contradictions by using concept hierarchies associated with the data. We evaluate the effectiveness of our approach with experiments on Web usage analysis.
Fichier principal
Vignette du fichier
IJUFKS09-1.pdf (344.77 Ko) Télécharger le fichier
Origine Fichiers éditeurs autorisés sur une archive ouverte
Loading...

Dates et versions

lirmm-00401364 , version 1 (20-03-2019)

Identifiants

Citer

Haoyuan Li, Anne Laurent, Pascal Poncelet. Discovering Fuzzy Unexpected Sequences with Concept Hierarchies. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2009, 17 (supp01), pp.113-134. ⟨10.1142/S0218488509006054⟩. ⟨lirmm-00401364⟩
85 Consultations
86 Téléchargements

Altmetric

Partager

More