A novel approach to prune mined association rules in large databases

Association rule mining finds interesting associations and/or correlation relationships among large set of data items. Association rules shows attribute value conditions that occur frequently together in a given dataset. However, the usefulness of association rules is strongly limited by the huge amount of delivered rules. 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. This paper presents a survey of different association rule mining techniques for market basket analysis, highlighting strengths of different association rule mining techniques. Further, 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 evaluation will help decision maker for making important decisions.