An Efficient Approach for Mining Association Rules in Large Databases

Association rules are if/then statements that help uncover relationships between seemingly unrelated data in a relational database or other information repository. In data mining, association rules are useful for analyzing and predicting customer behavior. They play an important part in shopping basket data analysis, product clustering, catalog design and store layout. However, when the number of association rules become large, it becomes less interesting to the user. It is crucial to help the decision-maker with an efficient postprocessing step in order to select interesting association rules throughout huge volumes of discovered rules. This motivates the need for association analysis. Thus, this paper presents a novel approach to prune mined association rules in large databases. Further, an analysis of different association rule mining techniques for market basket analysis, highlighting strengths of different association rule mining techniques are also discussed. We want to point out potential pitfalls as well as challenging issues need to be addressed by an association rule mining technique. We believe that the results of this approach will help decision maker for making important decisions.