Frequent itemsets mining based on Apriori-bit.

This paper proposed an Apriori frequent itemset mining based on bittable and inverted index (A-FIMBII). A-FIM-BII built the index from the collection of items to the transaction, and used priori nature to reduce the generation of candidate sets, calculating the support counts of two items through the bit operations. It compared to Apriori, ECLAT in four datasets. The experiment results show that A-FIMBII has a higher efficiency. [ABSTRACT FROM AUTHOR]