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Metaheuristics for data mining: survey and opportunities for big data

Author

Listed:
  • Clarisse Dhaenens

    (Univ. Lille, CNRS, Centrale Lille)

  • Laetitia Jourdan

    (Univ. Lille, CNRS, Centrale Lille)

Abstract

In the context of big data, many scientific communities aim to provide efficient approaches to accommodate large-scale datasets. This is the case of the machine-learning community, and more generally, the artificial intelligence community. The aim of this article is to explain how data mining problems can be considered as combinatorial optimization problems, and how metaheuristics can be used to address them. Four primary data mining tasks are presented: clustering, association rules, classification, and feature selection. This article follows the publication of a book in 2016 concerning this subject (Dhaenens and Jourdan in Metaheuristics for big data, Wiley, Hoboken, 2016), and an article published in 4OR (Dhaenens and Jourdan in 4OR 17 (2):115–139, 2019); additionally, updated references and an analysis of the current trends are presented.

Suggested Citation

  • Clarisse Dhaenens & Laetitia Jourdan, 2022. "Metaheuristics for data mining: survey and opportunities for big data," Annals of Operations Research, Springer, vol. 314(1), pages 117-140, July.
    • Handle: RePEc:spr:annopr:v:314:y:2022:i:1:d:10.1007_s10479-021-04496-0
    DOI: 10.1007/s10479-021-04496-0
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    References listed on IDEAS

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    1. Olafsson, Sigurdur & Li, Xiaonan & Wu, Shuning, 2008. "Operations research and data mining," European Journal of Operational Research, Elsevier, vol. 187(3), pages 1429-1448, June.
    2. Meisel, Stephan & Mattfeld, Dirk, 2010. "Synergies of Operations Research and Data Mining," European Journal of Operational Research, Elsevier, vol. 206(1), pages 1-10, October.
    3. Youcef Gheraibia & Abdelouahab Moussaoui & Sohag Kabir & Smaine Mazouzi, 2016. "Pe-DFA: Penguins Search Optimisation Algorithm for DNA Fragment Assembly," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 7(2), pages 58-70, April.
    4. Corne, David & Dhaenens, Clarisse & Jourdan, Laetitia, 2012. "Synergies between operations research and data mining: The emerging use of multi-objective approaches," European Journal of Operational Research, Elsevier, vol. 221(3), pages 469-479.
    5. Clarisse Dhaenens & Laetitia Jourdan, 2019. "Metaheuristics for data mining," 4OR, Springer, vol. 17(2), pages 115-139, June.
    6. Ahmad Abubaker & Adam Baharum & Mahmoud Alrefaei, 2015. "Automatic Clustering Using Multi-objective Particle Swarm and Simulated Annealing," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-23, July.
    7. de la Iglesia, B. & Richards, G. & Philpott, M.S. & Rayward-Smith, V.J., 2006. "The application and effectiveness of a multi-objective metaheuristic algorithm for partial classification," European Journal of Operational Research, Elsevier, vol. 169(3), pages 898-917, March.
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    Cited by:

    1. Qiyi He & Jin Tu & Zhiwei Ye & Mingwei Wang & Ye Cao & Xianjing Zhou & Wanfang Bai, 2023. "Association Rule Mining through Combining Hybrid Water Wave Optimization Algorithm with Levy Flight," Mathematics, MDPI, vol. 11(5), pages 1-19, February.

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