| Data Mining (DM) is a technique that aims to analyze and understand large source data and reveal knowledge hidden in the data. It has been viewed as an important evolution in information processing. During the past decade or over, the concepts and techniques on data mining have been presented, and especially in the latest few years. Some of them have been discussed in higher levels. Data Mining uses the classification, association, sequence analysis, clustering analysis, machine self-study and other statistic approaches to find potential, unaware and useful information and knowledge from large database. It is a new subject that involves a lot of subjects and develops with these subjects. Data Mining system can find lots of patterns, in which association rules that describe the interesting relations among the items in given data sets are the important area. This thesis is focus on the correlative study of association rule data miningAlgorithm is key part in D M. On the one hand, Data Mining faces large database, so the efficiency of algorithm is the most important; On the other hand, the computer in use does not meet the demand of the processing of large database. Consequently, we should research and improve present algorithms in order to make them be applied effectively and widely. Based on above, this thesis mainly studies the algorithm of Data Mining.Firstly, 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.Secondly, 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 analyses FP_growth algorithm . Following, details of the proposed algorithm IApriori (Improved Apriori) algorithm, theoretical basis, and the algorithm Apriori algorithm and FP_growth comparison The pseudo-code is given algorithm, the algorithm final excavation for a detailed explanation steps, 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. |
