Mining association rules to discover calendar based temporal classification

A well-known approach to knowledge discovery in databases involves the identification of association rules linking database attributes. Extracting all possible association rules from a database however is a computationally intractable problem, because of the combinatorial explosion in the number of sets of attributes for which incidence-counts must be computed. We study the problem of discovering association rules that display regular cyclic variation over time. For example, if we compute association rules over monthly sales data, we may observe seasonal variation where certain rules are true at approximately the same month each year. Similarly, association rules can also display regular hourly, daily, weekly, monthly etc., variation that is cyclical in nature. We demonstrate that existing methods cannot be naively extended to solve this problem. We then present two new algorithms for discovering such rules. The first one, which we call the sequential algorithm, treats association rules and cycles more or less independently. By studying the interaction between association rules and time, we devise a new technique called cycle rule, which reduces the amount of time needed to find association rules. The second algorithm, is transition algorithm, we demonstrate the effectiveness of the transition algorithm through a series of experiments.