Data Association Rules Mining Based On Extenics

As an emerging multi-disciplinary cross-application, data mining is playing an increasingly important role in supporting decision-making activities of most works of life. Discovering association rules is one of the most important tasks of data mining, that is, to find interesting associations or correlation relationships among a large set of data items. As massive amount of data continuously being collected and stored, knowledge or regular hidden in large data sets are more difficult to find. New methods and algorithms of data mining are provided by extension theory, and the new data mining thoughts are also stimulated by the extension set theory. The mainly research of the paper is as follows: we present association rule of extension theory by using the relativity of the extension about the theory.First of all, this paper systematically introduces theoretical framework and usual methods of extension theory, combines the element theory with algorithms of data mining, builds a model of database matter-element, simplifies complicated database into simple and intuitive database, make it expressed more clearly, and reduces the data volume of the next step.Second, from the view of traditional data mining algorithms, this article makes full use of extension theory, presents extension association rule algorithms of relational database bases on Apriori algorithm. Through multi-valued attributes Boolean value conversion on relational database, using association rules mining algorithms to mine association rules in relational databases to get a more valuable association rules.Finally, this paper fully summarizes the total paper and gives further research direction.