An improvement of fuzzy association rules mining algorithm based on redundacy of rules

摘要

In data mining approach, the quantitative attributes should be appropriately dealt with as well as the Boolean attributes. This paper presents a fast algorithm for extracting fuzzy association rules from database. The objective of the algorithm is to improve the computational time of mining for the actual application. In this paper, we propose a basic algorithm based on the Apriori algorithm for rule extraction utilizing redundancy of the extracted rules. The performance of the algorithm is evaluated through numerical experiments using benchmark data. From the results, the method is found to be promising in terms of computational time and redundant rule pruning.