Cyclic Association Rules

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, etc., variation that is cyclical in nature. We demonstrate that existing methods cannot be naively extended to solve this problem of cyclic association rules. 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 pruning,which reducesthe amount of time needed to find cyclic association rules. The second algorithm, which we call the interleaved algorithm, uses cycle pruning and other optimization techniques fo...

• Type: Conference
• Year: 1998
• Where: ICDE
• Authors: Banu Özden, Sridhar Ramaswamy, Abraham Silberschatz
• Keywords: Cyclic Association Rules, Database, ICDE 1998, Interleaved Algorithm, Treats Association Rules