Association rule mining is one of the most important procedures in data mining. In industry applications, often more than 10,000 rules are discovered. To allow manual insepection and support knowledge discovery the number of rules has to be reduced significantly by techniques such as pruning or grouping. In this paper, we present a new normalized distance metric to group association rules. Based on these distances, an agglomerative clustering algoritm is used to cluster the rules. Also the rules are embedded in a vector space by multi-dimensional scaling and clustered using a self organizing feature map. The results are combined for visualization. We compare various distance measures and illustrate subjective and objective cluster purity on results obtained from real data-sets.
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