Cyclic Association Rules

We study the problem of discovery association rules that display regular cyclic variation over time. For example, if we computmpute association rules over monthly sales data, we may observe seasonal variation where certain rules are true at approximately the same month each year. We demonstrate the effectiveness of the interleaved algorithm through a series of experiments.