CCMine: Efficient mining of confidence-closed correlated patterns

Won Young Kim, Young Koo Lee, Jiawei Han

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


Correlated pattern mining has become increasingly important recently as an alternative or an augmentation of association rule mining. Though correlated pattern mining discloses the correlation relationships among data objects and reduces significantly the number of patterns produced by the association mining, it still generates quite a large number of patterns. In this paper, we propose closed correlated pattern mining to reduce the number of the correlated patterns produced without information loss. We first propose a new notion of the confidence-closed correlated patterns, and then present an efficient algorithm, called CCMine, for mining those patterns. Our performance study shows that confidence-closed pattern mining reduces the number of patterns by at least an order of magnitude. It also shows that CCMine outperforms a simple method making use of the the traditional closed pattern miner. We conclude that confidence-closed pattern mining is a valuable approach to condensing correlated patterns.

Original languageEnglish (US)
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings
EditorsHonghua Dai, Ramakrishnan Srikant, Chengqi Zhang
Number of pages11
ISBN (Print)354022064X, 9783540220640
StatePublished - 2004
Event8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004 - Sydney, Australia
Duration: May 26 2004May 28 2004

Publication series

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


Other8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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