The Research And Application Of Association Rules In Data Mining

As one of the important contents in data mining,association rule mining aims to discover the interesting connection or the correlation midst a set of objects in a database.Frequent itemset mining is a very important phase of discovering association rules. To some degree, it decides the efficiency of association rule mining algorithm. It also plays an important role in other data mining tasks such as strong rules, correlation and sequence pattern. This thesis generally discusses the Data Mining, including the concepts and the pattern of the Data Mining, main mining problems, systemic classification, the application and development trend of the Data Mining.This thesis deeply researches the Association Rule Algorithm, which is important in the Data Mining. It analyses Apriori algorithm, which is classic in the Association Rules Algorithms, and the improved algorithms of Apriori, then We have brought forward one kind of the across linker based improvement method.Following, details of the proposed improved Apriori algorithm, theoretical basis, and the Apriori algorithm and improved Apriori algorithm comparison The pseudo-code is given algorithm, the algorithm final excavation for a detailed explanation steps.This article is also experimental system - the "secondary electrical terminal Pai-aided design system" was introduced. Because in the data base have stored the large amount of manufacturer and project in the previously information,Carries on the excavation to the database, discovers some and between some components or the line and the line connection,use the ordinary method to scan database its performance and the efficiency will be very low,but the use of association rules algorithm can solve the problems. In use .Net realized on the basis of these two algorithms. Two algorithms were used for the two representative data for testing. An analysis of the test results.