Association Rules Mining and Their Principal Analysis Component Based on Probability and Statistics Estimate Model

Currently those algorithms to mine association rules only pay attention to one aspect of efficiency or accuracy or correlativity respectively, even they ignore mining principal factors among all the correlativity. Thus, there seems a paradox among efficiency, accuracy and correlativity. In order to resolve to this conflict, a novel algorithm based on Probability estimate and principal component analysis 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 and accuracy, and principal component analysis helps us to know which factors have most influence to event rate so as to distinguish correlativity. Experimental results have demonstrated that our algorithms are efficient accurate and correlativity.