Multi-relational Bayesian Classification through Genetic Approach

Classification is an important subject in data mining and machine learning, which has been studied extensively and has a wide range of applications. Classification based on association rules, also called associative classification, is a technique that uses association rules to build classifier. CMAR employs a novel data structure, association rule, to compactly store and efficiently retrieve a large number of rules for classification. Association rule is a prefix rule structure to explore the sharing among rules, which achieves substantial compactness. To speed up the mining of complete set of rules, CMAR adopts a variant of recently developed FPgrowth method. FP-growth is much faster than Apriori-like methods used in previous association-based classification, such as especially when there exist a huge number of rules, large training data sets, and long pattern rules. We use classification using association rules not only to solve classification problems, but also to compare the quality of different association rule mining approaches. In this context we show that the quality of rule sets from the standard algorithm for association rule mining can be improved by using a different association rule mining strategy Above classification rate is 80%( MAX) hence the 20% data are unclassified. This is a challenge in the field of data classification. In this paper, we used multiple relational Bayesian classification algorithm based on genetic algorithm used for optimization of classification rate, generated by association rule.