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 itemset interactions, a task
which cannot be undertaken by current association
|Cite as: Liang, P., Roddick, J.F., Ceglar, A., Shillabeer, A. and de Vries, D. (2009). Discovering Itemset Interactions. In Proc. Thirty-Second Australasian Computer Science Conference (ACSC 2009), Wellington, New Zealand. CRPIT, 91. Mans, B., Ed. ACS. 121-128. |