Fuzzy association rules mining algorithm based on equivalence redundancy of items

In data mining approaches, quantitative attributes should be appropriately dealt with as well as Boolean attributes. This paper presents an essential improvement for extracting fuzzy association rules from a database. The objective of this paper is to improve the computational time of mining and to prune extracted redundant rules simultaneously for an actual data mining application. In this paper, we define the equivalence redundancy of fuzzy items and related theorems as a new concept for fuzzy data mining. Then, we propose a basic algorithm based on the Apriori algorithm for rule extraction utilizing the equivalence redundancy of the fuzzy items based on redundancy concepts of fuzzy association rules. The essential 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.