A Novel Fuzzy Positive and Negative Association Rules Algorithm

被引：0
|
作者：
Hu Kai [1 ]
机构：
[1] China Ship Dev & Design Ctr, Wuhan, Peoples R China
来源：
PROCEEDINGS OF THE NINTH INTERNATIONAL SYMPOSIUM ON DISTRIBUTED COMPUTING AND APPLICATIONS TO BUSINESS, ENGINEERING AND SCIENCE (DCABES 2010) | 2010年
关键词：
data mining; fuzzy association rules; membership function; multi-level fuzzy support; correlation coefficient;
D O I：
10.1109/DCABES.2010.163
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
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.
引用
收藏
页码：623 / 628
页数：6
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