Mining Direct and Indirect 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 has three limitations. Firstly, it can not 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 direct 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 direct and indirect fuzzy association rules with multiple minimum supports to resolve these three limitations.
data mining fuzzy association rules multiple minimum supports
Weimin Ouyang Qinhua Huang
Modern Education Technology Center Shanghai University of Political Science and Law Shanghai 201701, China
国际会议
上海
英文
978-982
2011-07-26（万方平台首次上网日期，不代表论文的发表时间）