Itemsets, which are treated as intermediate results in association mining, have attracted significant research due to the inherent complexity of their generation. However, there is currently little literature focusing upon the interactions between itemsets, the nature of which may potentially contain valuable information. This paper presents a novel tree-based approach to discovering item-set interactions, a task which cannot be undertaken by current association mining techniques
