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Volume 11 Issue 12
Dec.  2024

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2024  >  11(12): 2435-2450
J. Xu, Z. Zhang, Z. Lin, Y. Chen, and  W. Ding,  “Multi-view dynamic kernelized evidential clustering,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 12, pp. 2435–2450, Dec. 2024. doi: 10.1109/JAS.2024.124608
Citation: J. Xu, Z. Zhang, Z. Lin, Y. Chen, and  W. Ding,  “Multi-view dynamic kernelized evidential clustering,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 12, pp. 2435–2450, Dec. 2024. doi: 10.1109/JAS.2024.124608
shu

Multi-View Dynamic Kernelized Evidential Clustering

doi: 10.1109/JAS.2024.124608
Funds:  This work was supported in part by the Youth Foundation of Shanxi Province (5113240053), the Fundamental Research Funds for the Central Universities (G2023KY05102), the Natural Science Foundation of China (61976120), the Natural Science Foundation of Jiangsu Province (BK20231337), and the Natural Science Key Foundation of Jiangsu Education Department (21KJA510004)
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    Abstract
  • It is challenging to cluster multi-view data in which the clusters have overlapping areas. Existing multi-view clustering methods often misclassify the indistinguishable objects in overlapping areas by forcing them into single clusters, increasing clustering errors. Our solution, the multi-view dynamic kernelized evidential clustering method (MvDKE), addresses this by assigning these objects to meta-clusters, a union of several related singleton clusters, effectively capturing the local imprecision in overlapping areas. MvDKE offers two main advantages: firstly, it significantly reduces computational complexity through a dynamic framework for evidential clustering, and secondly, it adeptly handles non-spherical data using kernel techniques within its objective function. Experiments on various datasets confirm MvDKE’s superior ability to accurately characterize the local imprecision in multi-view non-spherical data, achieving better efficiency and outperforming existing methods in overall performance.

     

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    • 1 Clustering methods based on credal partition are referred to as evidential clustering.
    2 Supplementary Meterial of this paper can be found in links https://github.com/JinyiXUres/MvDKE
    3 https://archive.ics.uci.edu/ml/datasets.
    4 “−” indicates that the method is unable to obtain a valid clustering result on this dataset.
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    Highlights

    • We propose a multi-view evidential clustering algorithm that can handle overlapping areas between clusters
    • The method can characterize imprecision and handle non-spherical data
    • We provide a strategy to explore the potential structure of non-spherical data
    • We provide a dynamic framework to reduce the computational complexity

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