Research And Application Of Conditional Independences In Association Rule Mining

Because of broadly using of information technologies, the conflict between explosion of data and poorness of knowledge has been more and more acute, and the necessity of data mining becomes more and more urgent too. Among all branches of data mining, the research of association rule mining was the deepest one and the application of association rule mining was the most widely used one. Research of association rule mining almost concentrated on the optimality of frequent sets generation and the reduction of scanning times of transaction sets. Most of them were based on support-confidence framework. For the instinct shortcoming of the frame, few association rules were interesting. So the exploration of how to improve the interest of association rules became a novel and popular task in the research of association rule mining. Several experts employed new measures to improve the contentness and interest of association rules without adopting support-confidence framework in association rule mining. Under such backgrounds, this paper makes a study of the algorithmic framework of association rule mining based on conditional independences; explores how to perform post-processing and how to improve the interest of association rules with constraints of conditional independences after the processing of traditional association rule mining.Firstly, this paper explores main ideas and popular algorithms of traditional association rule mining, and analyzes different pruning technologies of frequent sets and different measures of interest. Secondly, this paper puts forward the algorithmic framework of association rule mining using Markov Blanket, and also explores each components of algorithmic framwork. Thirdly, this paper gives out a method of discovering Markov Blanket of multi-sets rather singleton, and proves the correctness of the method, afterwards this paper expresses it using Bayes Network too. Finally, aimed at the application of educational evaluation, this paper performs post-processing at those rules mined by Apriori algorithm, and filters those rules with the constraints of conditional independences, and reads out conditional independences from rules.