Mining hyperclique patterns: A summary of results

Hui Xiong, Pang Ning Tan, Vipin Kumar, Wenjun Zhou

Research output: Chapter in Book/Report/Conference proceedingChapter

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.

Original languageEnglish (US)
Title of host publicationData Mining Patterns
Subtitle of host publicationNew Methods and Applications
PublisherIGI Global
Pages57-84
Number of pages28
ISBN (Print)9781599041629
DOIs
StatePublished - 2007

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