Multidimensional inter-transactional association rules extend the traditional association rules to describe more general associations among items with multiple properties across transactions. “After McDonald and Burger King open branches, KFC will open a branch two months later and one mile away” is an example of such rules. Since the number of potential inter-transactional association rules tends to be extremely large, mining inter-transactional associations poses more challenges on efficient processing than mining traditional intra-transactional associations. In order to make such association rule mining truly practical and computationally tractable, in this study we present a template model to help users declare the interesting multidimensional inter-transactional associations to be mined. With the guidance of templates, several optimization techniques, i.e., joining, converging, and speeding, are devised to speed up the discovery of inter-transactional association rules. We show, through a series of experiments on both synthetic and real-life data sets, that these optimization techniques can yield significant performance benefits.
