Mining quantitative maximal hyperclique patterns: A summary of results

Yaochun Huang, Hui Xiong, Weili Wu, Sam Y. Sung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations


Hyperclique patterns are groups of objects which are strongly related to each other. Indeed, the objects in a hyperclique pattern have a guaranteed level of global pairwise similarity to one another as measured by uncentered Pearson's correlation coefficient. Recent literature has provided the approach to discovering hyperclique patterns over data sets with binary attributes. In this paper, we introduce algorithms for mining maximal hyperclique patterns in large data sets containing quantitative attributes. An intuitive and simple solution is to partition quantitative attributes into binary attributes. However, there is potential information loss due to partitioning. Instead, our approach is based on a normalization scheme and can directly work on quantitative attributes. In addition, we adopt the algorithm structures of three popular association pattern mining algorithms and add a critical clique pruning technique. Finally, we compare the performance of these algorithms for finding quantitative maximal hyperclique patterns using some real-world data sets.

Original languageEnglish (US)
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 10th Pacific-Asia Conference, PAKDD 2006, Proceedings
Number of pages5
StatePublished - 2006
Event10th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2006 - Singapore, Singapore
Duration: Apr 9 2006Apr 12 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3918 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other10th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2006

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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