Study On The Approach Of Association Rules Based On Triangular Fuzzy Number

Data Mining is an information processing technology,which is developing very fast in recent years.Using Data Mining,people can abstract information and knowledge from a great deal of data which is incomplete,noisy,dark and random.The information and knowledge we got was ignored and had not been known before but potentially useful.Association rule mining is an important sub-branch of the Data Mining,which mines interesting association relationships among a large set of data items.Association rules are considerd interesting if they satisfy both a minimum support threshold and a minimum confidence threshold.Association rule mining has become a hot research topic in recent years. Apriori algorithm is the classic association rule mining algorithm.There are two issues in the concrete association rule mining:(1)Using Apriori algorithm,the items are not distinguished.It is uncertain whether the final pattern will omit some important pattern or not.(2)Considersing the criteria of the items,how to consider and divide them? According to the above problems,this paper proposes a new association rule Mining algorithm based Triangular Fuzzy Number and Apriori algorithm.Compared the new algorithm with Apriori algorithm under PRETI(Platform for Research and Experiments in the Treatment of Information).The new algorithm is better than Apriori algorithm after the experiment.So the new algorithm is provable and effective.With the development and application of Data Mining and Data Warehouse,the satisfaction degree of customers are more and more concerned by the company.The concrete theory of Intuitionistie fuzzy set theory can satisfy the customers' mind.So this paper combines the concrete theory of Intuitionistic fuzzy set theory,α-cuts and Apriori algorithm to fred the satisfaction association rules.This new algorithm is proved effective and feasible.