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Mining Unexpected Sequential Patterns and Implication Rules

Mining Unexpected Sequential Patterns and Implication Rules

Dong (Haoyuan) Li, Anne Laurent, Pascal Poncelet
Copyright: © 2010 |Pages: 18
ISBN13: 9781605667546|ISBN10: 1605667544|ISBN13 Softcover: 9781616924508|EISBN13: 9781605667553
DOI: 10.4018/978-1-60566-754-6.ch010
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MLA

Li, Dong (Haoyuan), et al. "Mining Unexpected Sequential Patterns and Implication Rules." Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection, edited by Yun Sing Koh and Nathan Rountree, IGI Global, 2010, pp. 150-167. https://doi.org/10.4018/978-1-60566-754-6.ch010

APA

Li, D. H., Laurent, A., & Poncelet, P. (2010). Mining Unexpected Sequential Patterns and Implication Rules. In Y. Koh & N. Rountree (Eds.), Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection (pp. 150-167). IGI Global. https://doi.org/10.4018/978-1-60566-754-6.ch010

Chicago

Li, Dong (Haoyuan), Anne Laurent, and Pascal Poncelet. "Mining Unexpected Sequential Patterns and Implication Rules." In Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection, edited by Yun Sing Koh and Nathan Rountree, 150-167. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-754-6.ch010

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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 the authors 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, the authors present the approach USER for mining unexpected sequential rules in sequence databases. They propose a belief-driven formalization of the unexpectedness contained in sequential data, with which we propose 3 forms of unexpected sequences. They 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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