Association Rules Mining Based on an Optimized Probability and Statistics Estimate Model

This paper has analysed the a priori algorithm performance, and has pointed out performance bottleneck question of the a priori algorithm. Currently those algorithms to mine association rules only pay attention to one aspect of efficiency or accuracy respectively. There is a paradox between efficiency and accuracy. In order to resolve to this conflict, a novel algorithm based on probability estimate and least square estimate is proposed to mine the association rules from database with the high correlativity and the high confidence. Probability estimate reduce the times of database scanning so as to increase efficiency; least square estimate is based on rigorous and classical mathematical model so as to enhance accuracy. Furthermore, we deduce a recurrence formula to resolve K-itemsets issue. Experimental results have demonstrated that our algorithm is not only efficient but also keeps the completion of frequent items.