Title :
Mining Positive and Negative Weighted Fuzzy Association Rules in Large Transaction Databases
Author_Institution :
Modern Educ. Technol. Center, Shanghai Univ. of Political Sci. & Law, Shanghai, China
fDate :
Nov. 30 2009-Dec. 1 2009
Abstract :
Association rules mining 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 cannot concern quantitative attributes; secondly, only the positive association rules are discovered; thirdly, it treat each item with the same significance although different item may have different significance. In this paper, we put forward a discovery algorithm for mining positive and negative fuzzy weighted association rules to resolve these three limitations.
Keywords :
data mining; database management systems; fuzzy set theory; association rules mining; binary attributes databases; data mining; knowledge discovery; large transaction databases; negative weighted fuzzy association rules; positive weighted fuzzy association rules; Association rules; Data mining; Educational technology; Filters; Fuzzy logic; Itemsets; Knowledge acquisition; Transaction databases; data; mining; rules;
Conference_Titel :
Knowledge Acquisition and Modeling, 2009. KAM '09. Second International Symposium on
Conference_Location :
Wuhan
Print_ISBN :
978-0-7695-3888-4
DOI :
10.1109/KAM.2009.170