Abstract
In data mining approach, the quantitative attributes should be appropriately dealt with as well as the Boolean attributes corresponding to various applications and target areas. This paper presents a fast algorithm for extracting fuzzy association rules from a massive database. The objective of the algorithm is to improve the computational time of mining for actual applications. In this paper, we define redundancy of fuzzy association rules as a new concept for mining and essential theorems concerning with the redundancy of fuzzy association rules. Then, we propose a basic algorithm based on the Apriori algorithm for rule extraction utilizing output attribute specifications and redundancy of the extracted rules. The performance of the algorithm is evaluated through numerical experiments using benchmark data. From the results, the proposed method is found to be promising in terms of computational time and redun dant rule pruning.
