Prototype And Modeling Of Association Rule Algorithm Based On Bitmap And Granular Computing

We present an association rule algorithm based on granular computing (Bit-Compu-AssocRule) that doesn't follow the generation-and-test strategy of Apriori algorithm It adopts the divide and conquers strategy, thus avoids the time consuming table scan to find and prune the item sets. It is the fast bit operations based on its corresponding granular for all the operations of finding large item sets from the datasets. We also develop a prototype and model of association rules based on bitmap and granular computing. Our research indicates that bitmap and granular computing can greatly improve the performance of association rule algorithm.