Mining temporal indirect associations

Abstract

This paper presents a novel pattern called temporal indirect association. An indirect association pattern refers to a pair of items that rarely occur together but highly depend on the presence of a mediator itemset. The existing model of indirect association does not consider the lifespan of items. Consequently, some discovered patterns may be invalid while some useful patterns may not be covered. To overcome this drawback, in this paper, we take into account the lifespan of items to extend the current model to be temporal. An algorithm, MG-Grvwth, that finds the set of mediators in pattern-growth manner is developed. Then, we extend the framework of the algorithm to discover temporal indirect associations. Our experimental results demonstrated the efficiency and effectiveness of the proposed algorithms. © Springer-Verlag Berlin Heidelberg 2006.