Grouping Association Rules Using Lift

Association rule mining is a well established and popular data mining method for finding local dependencies between items in large transaction databases. However, a practical drawback of mining and efficiently using association rules is that the set of rules returned by the mining algorithm is typically too large to be directly used. Clustering association rules into a small number of meaningful groups would be valuable for experts who need to manually inspect the rules, for visualization and as the input for other applications. Interestingly, clustering is not widely used as a standard method to summarize large sets of associations. In fact it performs poorly due to high dimensionality, the inherent extreme data sparseness and the dominant frequent itemset structure reflected in sets of association rules. In this paper, we review association rule clustering and their shortcomings. We then propose a simple approach based on grouping columns in a lift matrix and give an example to illustrate its usefulness.