The Study Of Association Rules Data Mining Based On Genetic Algorithm

Data mining had already played a key role in the decision of many industries. Association rules mining is a fruitful branch of the research in data mining. Association rules found in kinds of databases can help make decision, and the study of mining association rule has become an important research direction in the field.In this thesis, a potential property of the frequent itemsets was deduced based on the study in the techniques of the data mining and association rule mining,and Apriori algorithm was improved by this property. The concepts of interest itemsets and normal rule were proposed which excavated from the acquisition of specific needs of the users. Apriori algorithm was improved again based on the two concepts. Then, I_Apriori was proposed by combining the two improved algorithm. Then, Algorithm_BasedGA was proposed based on the advantages of the efficient global optimization of genetic algorithms.Finally, a data mining system was designed and implemented for a specific application example of assciation rule mining, whose core mining algorithms used Apriori algorithm, I_Apriori algorithm and Algorithm_BasedGA algorithm. The results showed the correctness and validity of the the two kind of improved algorithms, and they were superior to the traditional association rule mining algorithm in the comparision of running time on the whole.