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Maintenance algorithm for high average-utility itemsets with transaction deletion

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Abstract

High-utility itemset mining (HUIM) is an extension of traditional association-rule mining that can find profitable itemsets for decision-making. It faces, however, a limitation since the utility of an itemset increases along with the size of it. High-average utility itemset mining (HAUIM) provides a fair measure to find the average-utility of an itemset, which is more reasonable to design the sales strategies for making the efficient decision. Traditional algorithms of HAUIM mostly focus on mining high average-utility itemsets (HAUIs) from the static database. When the database size is changed, for example, transaction insertion/deletion, the discovered information is required to be updated, thus the updated database is necessary to be re-scanned for identifying the set of HAUIs in the batch manner. In this paper, we present an updating algorithm called FUP-HAUIMD to maintain the discovered HAUIs with transaction deletion. When some transactions in the database are deleted, the designed FUP-HAUIMD algorithm can easily update the discovered HAUIs without scanning the database all the time. The designed FUP-HAUIMD algorithm divides the itemsets into four cases based on the modified fast updated (MFUP) concept. The average-utility (AU)-list structure is further utilized to keep the necessary ramification for later mining progress. Experiments are then conducted to compare the designed FUP-HAUIMD algorithm with the state-of-the-art baseline algorithm running on the batch mode, and the developed approach shows better performance in terms of runtime, number of examined patterns, and scalability.

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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 Technical Project under grants No. KQJSCX20170726103424709 and JCYJ20170307151733005, by the National Science Funding of Guangdong Province under grant No. 2016A030313659, and by the Science Research Project of Guangdong Province under grant No. 2017A020220011.

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China

    Jerry Chun-Wei Lin & Yina Shao

  2. Department of Computing, Mathematics and Physics, Western Norway University of Applied Sciences (HVL), Bergen, Norway

    Jerry Chun-Wei Lin

  3. School of Natural Sciences and Humanities, Harbin Institute of Technology (Shenzhen), Shenzhen, China

    Philippe Fournier-Viger

  4. IMADA Lab, Southern Denmark University, Odense, Denmark

    Youcef Djenouri

  5. School of Architecture and Urban Planning, Harbin Institute of Technology (Shenzhen), Shenzhen, China

    Xiangmin Guo

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  1. Jerry Chun-Wei Lin
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  2. Yina Shao
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  3. Philippe Fournier-Viger
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  4. Youcef Djenouri
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  5. Xiangmin Guo
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Correspondence to Jerry Chun-Wei Lin.

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Lin, J.CW., Shao, Y., Fournier-Viger, P. et al. Maintenance algorithm for high average-utility itemsets with transaction deletion. Appl Intell 48, 3691–3706 (2018). https://doi.org/10.1007/s10489-018-1180-8

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  • DOI: https://doi.org/10.1007/s10489-018-1180-8

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