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Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Association rules are the main technique for data mining. Apriori algorithm is a classical algorithm of association rule mining. Lots of algorithms for mining association rules and their mutations are proposed on basis of apriori algorithm, but traditional algorithms are not efficient. For the two bottlenecks of frequent itemsets mining: the large multitude of candidate 2-itemsets, the poor efficiency of couting their support, this paper proposes a novel algorithm so called reduced apriori algorithm with tag (RAAT), which reduces one redundant pruning operations of C2. If the number of frequent 1-itemsets is n, then the number of connected candidate 2-itemsets is Cn 2, while pruning operations Cn 2. The novel algorithm decreases pruning operations of candidate 2-itemsets, thereby saving time and increasing efficiency.For the bottleneck:poor efficiency of couting support, RAAT optimizes subset operation, through the transaction tag to speed up support calculations. The experimental results obtained from tests show that RAAT outperforms original one efficiency.