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Efficient Mining of High Average-Utility Itemsets with Multiple Thresholds

  • Tsu-Yang Wu
  • Jerry Chun-Wei LinEmail author
  • Shifeng Ren
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 81)

Abstract

In this paper, we propose an efficient algorithm to discover HAUIs based on the compact average-utility list structure. A tighter upper-bound model is used to instead of the traditional auub model used in HAUIM to lower the upper-bound value. Three pruning strategies are also respectively developed to facilitate mining performance of HAUIM. Experiments show that the proposed algorithm outperforms the state-of-the-art HAUIM-MMAU algorithm in terms of runtime and memory usage.

Keywords

Data mining High average-utility itemsets List structure Multiple thresholds 

Notes

Acknowledgments

This research was partially supported by the National Natural Science Foundation of China (NSFC) under grant No. 61503092, by the Research on the Technical Platform of Rural Cultural Tourism Planning Basing on Digital Media under grant 2017A020220011, and by the Tencent Project under grant CCF-Tencent IAGR20160115.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Tsu-Yang Wu
    • 1
    • 2
  • Jerry Chun-Wei Lin
    • 3
    Email author
  • Shifeng Ren
    • 3
  1. 1.Fujian Provincial Key Laboratory of Big Data Mining and ApplicationsFujian University of TechnologyFuzhouChina
  2. 2.National Demonstration Center for Experimental Electronic Information and Electrical Technology EducationFujian University of TechnologyFuzhouChina
  3. 3.School of Computer Science and TechnologyHarbin Institute of Technology Shenzhen Graduate SchoolShenzhenChina

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