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Abstract

A system and method for discovering association rules that display regular cyclic variation over time is disclosed. Such association rules may apply over daily, weekly or monthly (or other) cycles of sales data or the like. A first technique, referred to as the sequential algorithm, treats association rules and cycles relatively independently. Based on the interaction between association rules and time, we employ a new technique called cycle pruning, which reduces the amount of time needed to find cyclic association rules. A second algorithm, the interleaved algorithm, uses cycle pruning and other optimization techniques for discovering cyclic association rules with reduced overhead.

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First Claim