Discovering Fuzzy Unexpected Sequences with Concept Hierarchies

Abstract : 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.
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Article dans une revue
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, World Scientific Publishing, 2009, N/A, pp.23
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00401364
Contributeur : Haoyuan Li <>
Soumis le : jeudi 2 juillet 2009 - 21:52:41
Dernière modification le : vendredi 9 février 2018 - 16:58:06

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  • HAL Id : lirmm-00401364, version 1

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Haoyuan Li, Anne Laurent, Pascal Poncelet. Discovering Fuzzy Unexpected Sequences with Concept Hierarchies. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, World Scientific Publishing, 2009, N/A, pp.23. 〈lirmm-00401364〉

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