Frequent Item Set Mining Algorithm Based on Bit Combination

A frequent item set mining algorithm based on bit combination is proposed in this paper. Frequent item set mining algorithm based on bit combination is an algorithm that searches for possible frequent item set by transforming data into binary bit representation and adding data representing the combination of regulatory elements step by step, and then mining frequent item set by bit and calculation. In the process of data mining, the algorithm is optimized by pruning, preprocessing and frequent item set culling. Because the recursive method used by traditional frequent item set mining algorithms such as FP-Growth algorithm can't effectively parallel a large number of data, the greatest advantage of this algorithm is that it facilitates the parallel computation of data and provides a new idea for improving the efficiency of frequent item set mining. The parallel acceleration of the algorithm is realized by using OpenMP technology to verify the parallel feasibility of the algorithm in this paper.