On the Discovery of Interesting Patterns in Association Rules

Many decision support systems, which utilize association rules for discovering interesting patterns, require the discovery of association rules that vary over time. Such rules describe complicated temporal patterns such as events that occur on the “first working day of every month.” In this paper, we study the problem of discovering how association rules vary over time. In particular, we introduce the idea of using a calendar algebra to describe complicated temporal phenomena of interest to the user. We then present algorithms for discovering culendric association rules, which are association rules that follow the patterns set forth in the user supplied calendar expressions. We devise various optimizations that speed up the discovery of calendric association rules. We show, through an extensive series of experiments, that these optimization techniques provide performance benefits ranging from 5% to 250% over a less sophisti-