Volume 9 Number 9 (Sep. 2014)
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JSW 2014 Vol.9(9): 2342-2347 ISSN: 1796-217X
doi: 10.4304/jsw.9.9.2342-2347

An Algorithm of Top-k High Utility Itemsets Mining over Data Stream

Tianjun Lu1, Yang Liu2, Le Wang3
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


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.

General Information

ISSN: 1796-217X (Online)
Frequency: Monthly
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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