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Frequent itemset mining is an important operation to return all itemsets in the transaction table, which occur as a subset of at least a specified fraction of the transactions. The existing algorithms cannot compute frequent itemsets on massive data efficiently, since they either require multiple-pass scans on the table or construct complex data structures which normally exceed the available memory on massive data. This paper proposes a novel precomputation-based frequent itemset mining (PFIM)doi:10.1109/access.2019.2902602 fatcat:3wwiy5pncnevra6uuy7x5iaj4m