A Fast Fuzzy Association Rules Mining Algorithm Utilizing Output Field Specification(<Special Issue>COMPUTATIONAL INTELLIGENCE)

Abstract

This paper presents fast algorithms for extracting fuzzy association rules from database. The objective of the algorithm is to reduce the extracted redundant rules for the actual application, in order to improve the computational efficiency of fuzzy association rules mining. In this paper, for extracting fuzy association rules, it is assumed that the consequent part of the fuzzy rule is speci-fied in advance, i. e. before starting mining computation. This assumption corresponds to actual problems, e.g. diagnostics problem of the process, quality control action of manufacturing, and so on. The algorithm is based on the Apriori algorithm for rule extraction of the specified output field or the output fuzzy set. From the results of numerical experiments, the algorithm is found to be ef-fective compared with the conventional method in terms of computational time and redundant rule pruning.