Mining Hyperclique Patterns: A Summary of Results
Hui Xiong (Rutgers University, USA), Pang-Ning Tan (Michigan State University, USA), Vipin Kumar (University of Minnesota, USA) and Wenjun Zhou (Rutgers University, USA)
Copyright: © 2008
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.