Periodic frequent pattern mining, the process of finding frequent patterns which occur periodically in databases, is an important data mining task for various decision making. Though several algorithms have been proposed for their discovery, most employ a two stage process to evaluate the periodicity of patterns. That is, by firstly deriving the set of periods of a pattern from its coverset, and subsequently evaluating the periodicity from the derived set of periods. This two step process thus make algorithms for discovering periodic frequent patterns both time and memory inefficient in the discovery process. In this paper, we present solutions to reduce both runtime and memory consumption in periodic frequent pattern mining. We achieve this by evaluating the periodicity of patterns without deriving the set of periods from their coversets. Our experimental results show that our proposed solutions are efficient both in reducing the runtime and memory consumption in the discovery of periodic frequent patterns.
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