Realizing New Hybrid Rough Fuzzy Association Rule Mining Algorithm (RFA) Over Apriori Algorithm

Abstract

AbstractAssociation rules shows us interesting associations among data items. And the procedure by which these rules are extracted and managed is known as association rule mining. Classical association rule mining had many limitations. Fuzzy association rule mining (Fuzzy ARM) is a better alternative of classical association rule mining. But fuzzy ARM also has its limitations like redundant rule generation and inefficiency in large mining tasks. Rough association rule mining (Rough ARM) seemed to be a better approach than fuzzy ARM. Mining task is becoming huge now days. Performing mining task efficiently and accurately over a large dataset is still a big challenge to us. This paper presents the realization of new hybrid mining method which has incorporated the concepts of both rough set theory and fuzzy set theory for association rule generation and shows comparative analysis with Apriori algorithm based on test results of the algorithm over popular datasets.KeywordsAssociation rule miningFuzzy association rule miningFuzzy c-means clusteringRough set theoryRough association rule miningAttribute reductionApriori algorithm