¹School of Software, Nanyang Institute of Technology, Nanyang, Henan 473000, China
²School of Innovation and Experiment, Dalian University of Technology, Liaoning, China 116024
³School of Information Engineering, Ningbo Dahongying University, Ningbo, Zhejiang, China 315175

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Abstract—Existing top-k high utility itemset (HUI) mining algorithms generate candidate itemsets in the mining process; their time & space performance might be severely affected when the dataset is large or contains many long transactions; and when applied to data streams, the performance of corresponding mining algorithm is especially crucial. To address this issue, propose a sliding window based top-k HUIs mining algorithm TOPK-SW; it first stores each batch data of current window as well as the items’ utility information to a tree called HUI-Tree, which ensures effective retrieval of utility values without re-scan the dataset, so as to efficiently improve the mining performance. TOPK-SW was tested on 4 classical datasets; results show that TOPK-SW outperforms existing algorithms significantly in both time and space efficiency, especially the time performance improves over 1 order of magnitude.

Index Terms—data stream, high utility itemset, frequent itemset, data mining, top-k

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Cite: Tianjun Lu, Yang Liu, Le Wang, "An Algorithm of Top-k High Utility Itemsets Mining over Data Stream," Journal of Software vol. 9, no. 9, pp. 2342-2347, 2014.