Abstract: In view of a series of problems existing in support update, window update mode and frequent k-itemset mining of traditional frequent itemset mining algorithm in data flow, which results in low efficiency of space and time, an efficient AO algorithm for mining frequent itemsets in data streams is improved. The algorithm uses the idea of sliding window to mine the data stream in blocks; when there is new data flowing in the full window, the residual insertion is used to update the data; and operation is used to solve the support degree of frequent k-itemsets, and the superset detection is combined in the mining process, which greatly improves the mining efficiency. The experimental results show that the algorithm has good superiority in both time and space efficiency.

Key words: data stream, superset checking, frequent itemsets, And Operation