Mining Positive and Negative Weighted Fuzzy Association Rules in Large Transaction Databases

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.