Mining fuzzy association rules: interestingness measure and algorithm

Abstract

Discovering association rules is an important task of data mining research. Mining traditional association rules is built upon transaction databases. This technique, however, has some limitations. Each transaction merely contains binary items with each item either present or absent in a transaction. Another limitation is that only positive association rules are discovered. Mining fuzzy association rules and discovering negative association rules have been developed to overcome these limitations, respectively. In this paper, we combine these two approaches to propose a novel approach for mining both positive and negative fuzzy association rules. The interestingness measure for both positive and negative fuzzy association rule is proposed, the algorithm for mining these rules is described.