Discovery of Unexpected Recurrence Behaviors in Sequence Databases

Haoyuan Li 1 Anne Laurent 1 Pascal Poncelet 1
1 TATOO - Fouille de données environnementales
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : The discovery of unexpected behaviors in databases is an interesting problem for many real-world applications. In previous studies, unexpected behaviors are primarily addressed within the context of patterns, association rules, or sequences. In this paper, we study the unexpectedness with respect to the fuzzy recurrence behaviors contained in sequence databases. We first propose the notion of fuzzy recurrence rule, and then present the problem of mining unexpected sequences that contradict prior fuzzy recurrence rules. We also develop, UFR, an algorithm for discovering the sequences containing unexpected recurrence behaviors. The proposed approach is evaluated with Web access log data.
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Haoyuan Li, Anne Laurent, Pascal Poncelet. Discovery of Unexpected Recurrence Behaviors in Sequence Databases. International Journal of Computer Information Systems and Industrial Management Applications, Machine Intelligence Research Labs (MIR Labs), 2010, 2, pp.279-288. ⟨lirmm-00798703⟩

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