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Kernel C-Means Clustering Algorithms for Hesitant Fuzzy Information in Decision Making

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Abstract

When facing clustering problems for hesitant fuzzy information, we normally solve them on sample space by using a certain hesitant fuzzy clustering algorithm, which is usually time-consuming or generates inaccurate clustering results. To overcome the issue, we propose a novel hesitant fuzzy clustering algorithm called hesitant fuzzy kernel C-means clustering (HFKCM) by means of kernel functions, which maps the data from the sample space to a high-dimensional feature space. As a result, the differences between different samples are expanded and thus make the clustering results much more accurate. By conducting simulation experiments on distributions of facilities and the twenty-first Century Maritime Silk Road, the results reveal the feasibility and availability of the proposed HFKCM algorithm.

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Acknowledgements

The authors thank the anonymous reviewers for their helpful comments and suggestions, which have led to an improved version of this paper. The work is supported by the National Natural Science Foundation of China (No. 71571123).

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  1. Institute of Sciences, PLA University of Science and Technology, Nanjing, 210007, China

    Chaoqun Li, Hua Zhao & Zeshui Xu

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  1. Chaoqun Li
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  2. Hua Zhao
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  3. Zeshui Xu
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Correspondence to Hua Zhao or Zeshui Xu.

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Li, C., Zhao, H. & Xu, Z. Kernel C-Means Clustering Algorithms for Hesitant Fuzzy Information in Decision Making. Int. J. Fuzzy Syst. 20, 141–154 (2018). https://doi.org/10.1007/s40815-017-0304-3

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