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Efficiently mining uncertain high-utility itemsets

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Abstract

Data mining consists of deriving implicit, potentially meaningful and useful knowledge from databases such as information about the most profitable items. High-utility itemset mining (HUIM) has thus emerged as an important research topic in data mining. But most HUIM algorithms can only handle precise data, although big data collected in real-life applications using experimental measurements or noisy sensors is often uncertain. In this paper, an efficient algorithm, named Mining Uncertain High-Utility Itemsets (MUHUI), is proposed to efficiently discover potential high-utility itemsets (PHUIs) in uncertain data. Based on the probability-utility-list (PU-list) structure, the MUHUI algorithm directly mines PHUIs without generating candidates, and can avoid constructing PU-lists for numerous unpromising itemsets by applying several efficient pruning strategies, which greatly improve its performance. Extensive experiments conducted on both real-life and synthetic datasets show that the proposed algorithm significantly outperforms the state-of-the-art PHUI-List algorithm in terms of efficiency and scalability, and that the proposed MUHUI algorithm scales well when mining PHUIs in large-scale uncertain datasets.

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Acknowledgments

This research was partially supported by the National Natural Science Foundation of China (NSFC) under Grant No.61503092, by the Shenzhen Peacock Project, China, under Grant KQC201109020055A, by the Natural Scientific Research Innova- tion Foundation in Harbin Institute of Technology under Grant HIT.NSRIF.2014100, and by the Shenzhen Strategic Emerging Industries Program under Grant ZDSY20120613125016389.

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town, Xili, Shenzhen, 518055, People’s Republic of China

    Jerry Chun-Wei Lin & Wensheng Gan

  2. School of Natural Sciences and Humanities, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town, Xili, Shenzhen, 518055, People’s Republic of China

    Philippe Fournier-Viger

  3. Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan, ROC

    Tzung-Pei Hong

  4. Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan, ROC

    Tzung-Pei Hong

  5. Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan, ROC

    Vincent S. Tseng

Authors
  1. Jerry Chun-Wei Lin
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  2. Wensheng Gan
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  3. Philippe Fournier-Viger
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  4. Tzung-Pei Hong
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  5. Vincent S. Tseng
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Corresponding author

Correspondence to Jerry Chun-Wei Lin.

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The authors declare that there are no conflicts of interest in this paper.

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This article does not contain any studies with human participants performed by any of the authors.

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Communicated by C.-H. Chen.

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Lin, J.CW., Gan, W., Fournier-Viger, P. et al. Efficiently mining uncertain high-utility itemsets. Soft Comput 21, 2801–2820 (2017). https://doi.org/10.1007/s00500-016-2159-1

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