Indirect association: Mining higher order dependencies in data

Abstract

This paper introduces a novel pattern called indirect association and examines its utility in various application domains. Existing algorithms for mining associations, such as Apriori, will only discover itemsets that have support above a user-defined threshold. Any itemsets with support below the minimum support requirement are filtered out. We believe that an infrequent pair of items can be useful if the items are related indirectly via some other set of items. In this paper, we propose an algorithm for deriving indirectly associated itempairs and demonstrate the potential application of these patterns in the retail, textual and stock market domains.