Mining Unexpected Sequential Patterns and Implication Rules

Abstract : As common criteria in data mining methods, the frequency-based interestingness measures provide a statistical view of the correlation in the data, such as sequential patterns. However, when we consider domain knowledge within the mining process, the unexpected information that contradicts existing knowledge on the data has never less importance than the regularly frequent information. For this purpose, we present the approach USER for mining unexpected sequential rules in sequence databases. We propose a belief-driven formalization of the unexpectedness contained in sequential data, with which we propose 3 forms of unexpected sequences. We further propose the notion of unexpected sequential patterns and implication rules for determining the structures and implications of the unexpectedness. The experimental results on various types of data sets show the usefulness and effectiveness of our approach.
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Chapitre d'ouvrage
Yun Sing Koh and Nathan Rountree. Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection, pp.20, 2009, Advances in Data Warehousing and Mining Book Series
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Haoyuan Li, Anne Laurent, Pascal Poncelet. Mining Unexpected Sequential Patterns and Implication Rules. Yun Sing Koh and Nathan Rountree. Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection, pp.20, 2009, Advances in Data Warehousing and Mining Book Series. 〈lirmm-00344758〉

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