Classification Method Based on Association Rule Mining

In the field of data mining very large amount of data is processed in order to get small amount of useful data. There are two important data mining techniques to optimize efficiency, namely association rule mining and classification rule mining. In Data Mining, Classification is the process of finding and applying a model to describe and distinguish data classes, concepts and values. This work is a survey of major classification methods based on association rule mining. After this study better comparison of various classification methods can be done. After studying this all classification methods now, we develop a new method that name, BCAR in which we first apply fp-growth algorithm for rule generation and then calculate chi square value of each rule for subset selection after selecting this subset does classification based on chi square analysis. Main purpose is to build association rule classifier without loss of performance & accuracy of the resultant classifier.