The Study Of Parallel Algorithm For Mining Association Rules And Application In Medicine Selling System

Data mining technology is used to help people finding the information and knowledge in the data. It has become the core technology of the intelligence commerce. It has been widely used in many areas and drawn the attention of the whole academe. How to improve the efficiency of data mining has become a popular issue for the academic circle to probe into. One important application of association rules discovery in data mining is sales behavior dicision. Apriori algorithm is the most influential algorithm in mining frequent itemset of association rules. However, it is known to have some defects, such as scanning database times, low efficiency. Based on the classical Apriori algorithm, the author proposes a new parallel Bi-Apriori algorithm. A especial data structure is used in this algorithm, which mining all the frequent k-itemsset only need two times scanning database. The experiment results show that the strategy can improve the speed greatly and converges at high rate, especially to a large training samples.The hardware platform is PC connected with LAN. The software platform is PVM and Windows. They construct the whole PC-cluster system. The parallel program model is master/slave model. The algorithm assign data set to each node realizes the data-parallel. The practical application of association rules in sales behavior dicision is introduced in this paper, and has discussed the results contrastively in different numbers of processor. The correctness and veracity of this algorithm has been validated. At last, the mining results has been analyzed based on the business and achieve a satisfactory effect . This algorithm is topical practical in the medical sales dataset has been proved.