DocumentCode :
1712777
Title :
Influence and conditional influence-new interestingness measures in association rule mining
Author :
Chen, Guoqing ; Liu, De ; Li, Jiexun
Author_Institution :
Sch. of Econ. & Manage., Tsinghua Univ., Beijing, China
Volume :
3
fYear :
2001
fDate :
6/23/1905 12:00:00 AM
Firstpage :
1440
Lastpage :
1443
Abstract :
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 the 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
Keywords :
data mining; fuzzy logic; fuzzy set theory; antecedent; association rule mining; conditional influence; confidence deviation; consequent; degrees of confidence; degrees of support; interestingness measures; negatively influential facts; Association rules; Data mining; Decision making; Filtering; Itemsets; Transaction databases;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems, 2001. The 10th IEEE International Conference on
Conference_Location :
Melbourne, Vic.
Print_ISBN :
0-7803-7293-X
Type :
conf
DOI :
10.1109/FUZZ.2001.1008930
Filename :
1008930
Link To Document :
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