CoMine: Efficient mining of correlated patterns

Young Koo Lee, Won Young Kim, Y. Dora Cai, Jiawei Han

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

Abstract

Association rule mining often generates a huge number of rules, but a majority of them either are redundant or do not reflect the true correlation relationship among data objects. In this paper, we re-examine this problem and show that two interesting measures, all confidence (denoted as α) and coherence (denoted as γ), both disclose genuine correlation relationships and can be computed efficiently. Moreover, we propose two interesting algorithms, CoMine(α) and CoMine(γ), based on extensions of a pattern-growth methodology. Our performance study shows that the CoMine algorithms have high performance in comparison with their Apriori-based counterpart algorithms.

Original languageEnglish (US)
Title of host publicationProceedings - 3rd IEEE International Conference on Data Mining, ICDM 2003
Pages581-584
Number of pages4
StatePublished - Dec 1 2003
Event3rd IEEE International Conference on Data Mining, ICDM '03 - Melbourne, FL, United States
Duration: Nov 19 2003Nov 22 2003

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other3rd IEEE International Conference on Data Mining, ICDM '03
Country/TerritoryUnited States
CityMelbourne, FL
Period11/19/0311/22/03

ASJC Scopus subject areas

  • Engineering(all)

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