A fast association rule algorithm based on bitmap and granular computing

Abstract

Mining association rules from databases is a time-consuming process. Finding the large item set fast is the crucial step in the association rule algorithm. In this paper we present a fast association rule algorithm (Bit-AssoRule) based on granular computing. Our Bit-AssocRule doesn't follow the generation-and-test strategy of Apriori algorithm and adopts the divide-and-conquer strategy, thus avoids the time-consuming table scan to rind and prune the itemsets, all the operations of finding large itemsets from the datasets are the fast bit operations based on its corresponding granular. The experimental result of our Bit-AssocRule algorithm with Apriori, AprioriTid and AprioirHybrid algorithms shows Bit-AssocRule is 2 to 3 orders of magnitudes faster. Our research indicates that bitmap and granular computing can greatly improve the performance of association rule algorithm, and are very promising for data mining applications.