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.

In this paper, we discuss some new bitmap techniques for optimizing decision support queries and association rule algorithm. We first show how to use a new type of predefined bitmap join index (prejoin/spl I.bar/bitmap/spl I.bar/index) to efficiently execute complex decision support queries with multiple outer join operations involved and push the outer join operations from the data flow level to the bitmap level and achieve significant performance gain. Then we discuss a bitmap based association rule algorithm. Our bitmap based association rule algorithm Bit-AssocRule doesn't follow the generation-and-test strategy of a priori algorithm and adopts the divide-and-conquer strategy, thus avoids the time-consuming table scan to find and prune the item sets, all the operations of finding large item sets from the datasets are the fast bit operations. The experimental results show Bit-AssocRule is 2 to 3 orders of magnitude faster than a priori and a priori hybrid algorithms. Our results indicate that bitmap techniques can greatly improve the performance of decision support queries and association rule algorithm, and bitmap techniques are very promising for the decision support query optimization and data mining applications.

It is well known that relational theory carries very little semantic. To mine deeper semantics, additional modeling is necessary. In fact, some "pure" association rules are found to exist even in randomly generated data. We consider a relational database in which every attribute value has some additional information, such as price, fuzzy degree, neighborhood, or security compartment and levels. Two types of additions are considered: one is structure added, the other is valued-added. Somewhat a surprise, the additional cost in semantics checking is found to be very well compensated by the pruning of non-semantic rules.