Influence and conditional influence - New interestingness measures in association rule mining

Guoqing Chen, De Liu, Jiexun Li

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

6 Scopus citations

Abstract

This paper discusses the issues of interestingness in association rule mining. First, a rule is possibly redundant or misleading even if it possesses high degrees of confidence and support. Second, association rules do not reflect the effect of negatively influential facts. Such problems are related to confidence deviation. In this paper, therefore, two new measures of interestingness, namely influence and conditional influence, are introduced to represent the effect of the antecedent on the consequent. Furthermore, the mining algorithms are extended accordingly such that certain redundant rules can be eliminated and negatively influential rules may be discovered.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Fuzzy Systems
Pages1440-1443
Number of pages4
Volume3
StatePublished - Dec 1 2001
Event10th IEEE International Conference on Fuzzy Systems - Melbourne, Australia
Duration: Dec 2 2001Dec 5 2001

Other

Other10th IEEE International Conference on Fuzzy Systems
CountryAustralia
CityMelbourne
Period12/2/0112/5/01

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  • Cite this

    Chen, G., Liu, D., & Li, J. (2001). Influence and conditional influence - New interestingness measures in association rule mining. In IEEE International Conference on Fuzzy Systems (Vol. 3, pp. 1440-1443)