Data association rules mining based on extension theory

Discovering association rules is one of the most important tasks of data mining, that is, to find interesting associations or correlation relationship among a large set of data items. This paper describes the basic theory of Extenics, combines it with the present mining method of association rule to build the basic-element model of a relational database. On the basis of Apriori algorithm, through converting a multi-valued characteristic to Boolean value, using association rule mining algorithms and Extenics correlation ideological, to carry out extension data association rule mining on a relation database and gain valuable association rules. And according to the implication and correlation of basic elements, to expand the association rules and generate the knowledge of extension transformation rules.