We propose a new methodology for visualizing association mining results. Inter-item distances are computed from combinations of item set supports. The new distances retain a simple pairwise structure, and are consistent with important frequently occurring item sets. Thus standard tools of visualization, e.g. hierarchical clustering dendrograms can still be applied, while the distance information upon which they are based is richer. Our approach is applicable to general association mining applications, as well as applications involving information spaces modeled by directed graphs, e.g. the Web. In the context of collections of hypertext documents, the inter-document distances capture the information inherent in a collection's link structure, a for of link mining. We demonstrate our methodology with document sets extracted fro the Science Citation Index, applying a metric that measures consistency between clusters and frequent itemsets.