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Unsupervised Attribute Reduction Algorithms for Multiset-Valued Data Based on Uncertainty Measurement

Author

Listed:
  • Xiaoyan Guo

    (School of Computer Science, Zhuhai College of Science Technology, Zhuhai 519000, China)

  • Yichun Peng

    (School of Computer Science and Engineering, Yulin Normal University, Yulin 537000, China
    Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China)

  • Yu Li

    (School of Alibaba Cloud Big Data Application, Zhuhai College of Science and Technology, Zhuhai 519041, China)

  • Hai Lin

    (College of Mathematics and Information Science, Guangxi University, Nanning 530004, China)

Abstract

Missing data introduce uncertainty in data mining, but existing set-valued approaches ignore frequency information. We propose unsupervised attribute reduction algorithms for multiset-valued data to address this gap. First, we define a multiset-valued information system (MSVIS) and establish θ -tolerance relation to form the information granules. Then, θ -information entropy and θ -information amount are introduced as uncertainty measures. Finally, these two UMs are used to design two unsupervised attribute reduction algorithms in an MSVIS. The experimental results demonstrate the superiority of the proposed algorithms, achieving average reductions of 50% in attribute subsets while improving clustering accuracy and outlier detection performance. Parameter analysis further validates the robustness of the framework under varying missing rates.

Suggested Citation

  • Xiaoyan Guo & Yichun Peng & Yu Li & Hai Lin, 2025. "Unsupervised Attribute Reduction Algorithms for Multiset-Valued Data Based on Uncertainty Measurement," Mathematics, MDPI, vol. 13(11), pages 1-25, May.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:11:p:1718-:d:1662946
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