¹College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
²Department of Software Engineering, Yancheng Teachers University, Yancheng, China
³Department of Computer Science, Yangzhou University, Yangzhou, China

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Abstract—Mining frequent itemsets from data stream is an important task in stream data mining. This paper presents an algorithm Stream_FCI for mining the frequent closed itemsets from data streams in the model of sliding window. The algorithm detects the frequent closed itemsets in each sliding window using a DFP-tree with a head table. In processing each new transaction, the algorithm changes the head table and modifies the DFP-tree according to the changed items in the head table. The algorithm also adopts a table to store the frequent closed itemsets so as to avoid the time-consuming operations of searching in the whole DFPtree for adding or deleting transactions. Our experimental results show that our algorithm is more efficient and has lower time and memory complexity than the similar algorithms Moment and FPCFI-DS.

Index Terms—Stream data, mining closed frequent data itemsets, sliding window.

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Cite: Keming Tang, Caiyan Dai, Ling Chen, "A Novel Strategy for Mining Frequent Closed Itemsets in Data Streams," Journal of Computers vol. 7, no. 7, pp. 1564-1573, 2012.