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Updating the Discovered High Average-Utility Patterns with Transaction Insertion

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Genetic and Evolutionary Computing (ICGEC 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 579))

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Abstract

In this paper, we propose an algorithm to handle the transaction insertion for efficiently updating the discovered high average-utility upper-bound itemsets (HAUUBIs) based on the average-utility (AU)-list structure and the Fast UPdated (FUP) concept. The proposed algorithm divides the HAUUBIs existing in the original database and new transactions into four cases, and each case can be respectively maintained to identify the actual high average-utility itemsets (HAUIs) without multiple database scans and enormous candidate generation. Experiments showed that the proposed algorithm has better performance compared to state-of-the-art algorithm in terms of runtime and generates the similar number of candidates.

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Acknowledgment

This research was partially supported by the National Natural Science Foundation of China (NSFC) under grant No. 61503092, by the Shenzhen Technical Project under JCYJ20170307151733005, by the Science Research Project of Guangdong Province under grant No. 2017A020220011, and by the National Science Funding of Guangdong Province under Grant No. 2016A030313659.

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Authors and Affiliations

  1. Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, 350118, China

    Tsu-Yang Wu

  2. National Demonstration Center for Experimental Electronic Information and Electrical Technology Education, Fujian University of Technology, Fuzhou, China

    Tsu-Yang Wu

  3. School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China

    Jerry Chun-Wei Lin & Yinan Shao

  4. School of Natural Sciences and Humanities, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China

    Philippe Fournier-Viger

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

    Tzung-Pei Hong

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

    Tzung-Pei Hong

Authors
  1. Tsu-Yang Wu
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  2. Jerry Chun-Wei Lin
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  3. Yinan Shao
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  4. Philippe Fournier-Viger
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  5. Tzung-Pei Hong
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Corresponding author

Correspondence to Jerry Chun-Wei Lin .

Editor information

Editors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China

    Jerry Chun-Wei Lin

  2. Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, Fujian, China

    Jeng-Shyang Pan

  3. School of Computer Science, Engineering and Mathematics, Flinders University, Bedford Park, South Australia, Australia

    Shu-Chuan Chu

  4. School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China

    Chien-Ming Chen

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Wu, TY., Lin, J.CW., Shao, Y., Fournier-Viger, P., Hong, TP. (2018). Updating the Discovered High Average-Utility Patterns with Transaction Insertion. In: Lin, JW., Pan, JS., Chu, SC., Chen, CM. (eds) Genetic and Evolutionary Computing. ICGEC 2017. Advances in Intelligent Systems and Computing, vol 579. Springer, Singapore. https://doi.org/10.1007/978-981-10-6487-6_9

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  • DOI: https://doi.org/10.1007/978-981-10-6487-6_9

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6486-9

  • Online ISBN: 978-981-10-6487-6

  • eBook Packages: EngineeringEngineering (R0)

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