Mining Hyperclique Patterns: A Summary of Results

Mining Hyperclique Patterns: A Summary of Results

Hui Xiong, Pang-Ning Tan, Vipin Kumar, Wenjun Zhou
Copyright: © 2008 |Pages: 28
ISBN13: 9781599041629|ISBN10: 1599041626|EISBN13: 9781599041643
DOI: 10.4018/978-1-59904-162-9.ch003
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MLA

Xiong, Hui, et al. "Mining Hyperclique Patterns: A Summary of Results." Data Mining Patterns: New Methods and Applications, edited by Pascal Poncelet, et al., IGI Global, 2008, pp. 57-84. https://doi.org/10.4018/978-1-59904-162-9.ch003

APA

Xiong, H., Tan, P., Kumar, V., & Zhou, W. (2008). Mining Hyperclique Patterns: A Summary of Results. In P. Poncelet, F. Masseglia, & M. Teisseire (Eds.), Data Mining Patterns: New Methods and Applications (pp. 57-84). IGI Global. https://doi.org/10.4018/978-1-59904-162-9.ch003

Chicago

Xiong, Hui, et al. "Mining Hyperclique Patterns: A Summary of Results." In Data Mining Patterns: New Methods and Applications, edited by Pascal Poncelet, Florent Masseglia, and Maguelonne Teisseire, 57-84. Hershey, PA: IGI Global, 2008. https://doi.org/10.4018/978-1-59904-162-9.ch003

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Abstract

This chapter presents a framework for mining highly-correlated association patterns named hyperclique patterns. In this framework, an objective measure called h-confidence is applied to discover hyperclique patterns. We prove that the items in a hyperclique pattern have a guaranteed level of global pairwise similarity to one another. Also, we show that the h-confidence measure satisfies a cross-support property which can help efficiently eliminate spurious patterns involving items with substantially different support levels. In addition, an algorithm called hyperclique miner is proposed to exploit both cross-support and anti-monotone properties of the h-confidence measure for the efficient discovery of hyperclique patterns. Finally, we demonstrate that hyperclique patterns can be useful for a variety of applications such as item clustering and finding protein functional modules from protein complexes.

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