Pfp: parallel fp-growth for query recommendation

  title={Pfp: parallel fp-growth for query recommendation},
  author={Haoyuan Li and Yi Wang and Dong Zhang and Ming Zhang and Edward Y. Chang},
Frequent itemset mining (FIM) is a useful tool for discovering frequently co-occurrent items. Since its inception, a number of significant FIM algorithms have been developed to speed up mining performance. Unfortunately, when the dataset size is huge, both the memory use and computational cost can still be prohibitively expensive. In this work, we propose to parallelize the FP-Growth algorithm (we call our parallel algorithm PFP) on distributed machines. PFP partitions computation in such a way… CONTINUE READING
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