Applied Research Of Associate Rule Based On Extension Theory

Data mining is a multidisciplinary field, drawing work from areas including database technology ,artificial intelligence ,machine learning,neural networks,stastics,pattem recognition ,knowledge-based systems,knowledge acquisition, information retrieval.high-performance computing, and data visualization. Discovering association rules is one of the most important task in data mining, At present, the core of the most association rule is the algorithm.The main target is Boolean association rules.The mainly research of the paper is two parts:we present association rule of extension theory by using the relativity of the extention theory; and conbining it and the present mining method of association rule puts forward mining algorithm of quantitative association rule.The creation in this paper is that we present an algorithm of association rules,discovery based on extension transformation and Apriori algorithm. The paper sets up the matter-element set ,according to data base, discusses the relation between characteristic and value .discoveries the valueable association based on extension space etc, data compression removing redundant data.Cornbining rough set and concept tree, the paper does corresponding discrete mapping for relavite data.