Research Of Extended Concepts For Association Rule Discovery

Data mining has received considerable interest (Fayyad & Uthurusamy, 1996),of which the quintessential problem in database research has been association rule mining(Agrawalet al..,1993). Today the mining of such rules is still one of the most popular pattern discover method in KDD.In brief, an association rule is an expression X=>Y, where X and Y are sets of items. The meaning of such rules is quite intuitive: Given a database D of transactions –where each transaction is a set of items-X=>Y expresses that whenever a transaction T contains X than T probably contains Y also.In this paper we deal with the algorithmic aspects of association rule mining .In fact, a broad variety of efficient algorithms to mine association rules have been developed during the last years. These approaches are more or less described separately in the corresponding literature .To overcome this situation we gibe a general survey of the basic ideas behind association rule mining in chart 1,2.In the following sections, we propose several extended association rules algorithms. In chapter 3, we introduction a weighted association rules which affect the computation of the support. In this extended model, transactions contain information that is of different importance to the user.In chapter 4, we propose another extended association rules—Ration Association Rules, which is the kernel section in this paper. Because the tradition Association Rule algorithms operate on a data matrix to derive association rules. That is ,the vast majority of association rule discovery technique are Boolean ,since they discard the quantities of the items bought and only pay attention to whether something was bought or not. Here, we propose a new paradigm, namely, Ratio Association Rules, which are quantifiable in that we can measure the "goodness" of a set of discovered rules. In fact, we have proved that our algorithm works very well in mining traditional association rules. What's more, we can illustrate some contributions about such algorithm. For example, using such method, we reconstruct missing data which is very prevail problems, and we can detection some outlier in our original data matrix such as noise, fraud data etc. In the last section, we introduction our data mining platform which is a important part of our National 863 Projection. In the platform, we can select some kinds of data mining algorithms to operate the database. Such as attribute reduction, classify, clustering. And so on.In the field of Data Mining, the research of association rules is carried abroad. Today, mining quantitative association rule is still in open which needs us to spend more time to research...