document: Out-of-core frequent pattern mining on a commodity PC In this work we focus on the problem of frequent itemset mining on large , out-of-core data sets . After presenting a characterization of existing out-of-core frequent itemset mining algorithms and their drawbacks , we introduce our efficient , highly scalable solution . Presented in the context of the FPGrowth algorithm , our technique involves several novel I\/O-conscious optimizations , such as approximate hash-based sorting and blocking , and leverages recent architectural advancements in commodity computers , such as 64-bit processing . We evaluate the proposed optimizations on truly large data sets , up to 75GB , and show they yield greater than a 400-fold execution time improvement . Finally , we discuss the impact of this research in the context of other pattern mining challenges , such as sequence mining and graph mining .
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