Research On Association Rule Data Mining Based On Fuzzy Set Theory

With the development of information technology, Data Mining has been paidattention extensively. As we know, Data Mining has a large research scope,Association rules data mining is one of the important research subject in it. Deeplyresearching into the subject has the most important values not only in theory but alsoin applications. Association rule mining is put forward by Agrawal and the others in1993, Firstly the purpose is analyzing the relation of items in transaction database,later, reserarchers improved and extended the prototype of the question. At present,association rules technology has been applied in business, telecommunication,finance, agriculture, medical treatment and so on. It has brought a good effect.In the research of association rules data mining, the algorithms research is itsimportant part for mining association rules. Many algorithms in the field have beenput forward for mining association rules so far. One of them, the most famous isapriori algorithm presented by Agrawal.Apriori Algorithm belongs to indirect miningalgorithm and what it mines is the whole association rules of transaction database;The traditional data mining algorithms, such as Apriori algorithm and its improvedalgorithms, are focus on the mining in accurate concepts, and can not mine theinaccurate or fuzzy concepts. So, the thesis integrates the fuzzy-set concepts with datamining algorithms to make a deeper research on association rules mining algorithms,and introduces the concept of fuzzy association rules, which expresses the associatedrelationship by fuzzy concepts and extends the range of the application anddescription of association rules. The thesis introduces a fuzzy multilevel associationrules mining algorithm based on the multilevel association rules mining algorithm,which will be applied to the commodity exchange and used to mine association rulesin fuzzy concepts.It will be helpful to instruct decision-making.In the current researches of association rules, all the items in a database aretreated in a uniform way. However, it is not true in the real world databases, in which different items usually have different importances.Calculating weight through thefrequency of item attribute aimed at deficiency which exists in the traditional Apriorialgorithm. Improving algorithms for mining fuzzy weighted association rules basedon fuzzy sets theories. After this, the feasibility and performance of the algorithmsthrough the experiments are discussed.