Mining maximal hyperclique pattern: A hybrid search strategy

Yaochun Huang, Hui Xiong, Weili Wu, Ping Deng, Zhongnan Zhang

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

A hyperclique pattern is a new type of association pattern that contains items which are highly affiliated with each other. Specifically, the presence of an item in one transaction strongly implies the presence of every other item that belongs to the same hyperclique pattern. In this paper, we present an algorithm for mining maximal hyperclique patterns, which specifies a more compact representation of hyperclique patterns and are desirable for many applications, such as pattern-based clustering. Our algorithm exploits key advantages of both the Depth First Search (DFS) strategy and the Breadth First Search (BFS) strategy. Indeed, we adapt the equivalence pruning method, one of the most efficient pruning methods of the DFS strategy, into the process of the BFS strategy. Our experimental results show that the performance of our algorithm can be orders of magnitude faster than standard maximal frequent pattern mining algorithms, particularly at low levels of support.

Original languageEnglish (US)
Pages (from-to)703-721
Number of pages19
JournalInformation Sciences
Volume177
Issue number3
DOIs
StatePublished - Feb 1 2007

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence

Keywords

  • Association rules
  • Data mining
  • Hyperclique patterns

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