Mining Positive and Negative Fuzzy Association Rules with Multiple Minimum Supports in Large Transaction Databases
Association rule is an important research topic in data mining and knowledge discovery.Traditional algorithms for mining association rules are built on the binary attributes databases,which at least has three limitations.Firstly,it cannot concern quantitative attributes; secondly,it finds out frequent itemsets based on the single one user-specified minimum support threshold,which implicitly assumes that all items in the data have similar frequency; thirdly,only the positive association rules are discovered.Mining fuzzy association rules has been proposed to address the first limitation.In this paper,we put forward a discovery algorithm for mining both positive and negative fuzzy association rules with multiple minimum supports to resolve these three limitations.
Data mining Fuzzy association rules Multiple minimum supports
Wei-Min OUYANG Qin-Hua HUANG
Modern Education Technology Center,Shanghai University of Political Science and Law 200438 Shanghai,China
国内会议
杭州
英文
1-6
2014-10-18（万方平台首次上网日期，不代表论文的发表时间）