A Variable Size Sliding Window Based Frequent Itemsets Mining Algorithm in Data Stream

Due to the unpredictability and the concept drift character of the data stream, the traditional sliding window is difficult to adapt to frequent itemsets mining in data stream. A new variable sliding window based VSW-SCPS algorithm is proposed. The algorithm maintains a tree structure of SCPS-tree in memory, which is the storage structure of sliding window. When data flowing in, the SCPS-tree will adjust dynamically, the window size will be adjusted by the detection of concept drift according to the result of FP-growth mining algorithm. Experimental results show that the proposed algorithm has good time efficiency.