DocumentCode
2061611
Title
A Novel Fuzzy Positive and Negative Association Rules Algorithm
Author
Kai, Hu
Author_Institution
China Ship Dev. & Design Center, Wuhan, China
fYear
2010
fDate
10-12 Aug. 2010
Firstpage
623
Lastpage
628
Abstract
According to the existing mining algorithm of fuzzy association rules, a novel fuzzy positive and negative association rules algorithm will be proposed in this paper. We focus on the membership function of fuzzy set and minimum support parameters of positive and negative association rules and adopt a method that selects parameters automatically which is based on the k-means clustering. Besides, multi-level fuzzy support and correlation coefficient are chosen to restrain the quantity and quality of rules generated by the algorithm. Finally the validity and accuracy of the algorithm are proved by an experiment.
Keywords
data mining; fuzzy set theory; pattern clustering; correlation coefficient; fuzzy negative association rules algorithm; fuzzy positive association rules algorithm; fuzzy set membership function; k-means clustering; mining algorithm; multilevel fuzzy support; Algorithm design and analysis; Association rules; Clustering algorithms; Correlation; Fuzzy sets; Itemsets;
fLanguage
English
Publisher
ieee
Conference_Titel
Distributed Computing and Applications to Business Engineering and Science (DCABES), 2010 Ninth International Symposium on
Conference_Location
Hong Kong
Print_ISBN
978-1-4244-7539-1
Type
conf
DOI
10.1109/DCABES.2010.163
Filename
5571527
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