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An orthogonal clustering method under hesitant fuzzy environment

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Abstract

In this paper, we investigate the cluster techniques of hesitant fuzzy information. Consider that the distance measure is one of the most widely used tools in clustering analysis, we first point out the weakness of the existing distance measures for hesitant fuzzy sets (HFSs), and then put forward a novel distance measure for HFSs, which involves a new hesitation degree. Moreover, we construct the distance matrix and choose different values ofλ so as to obtain theλ- cutting matrix, each column of which is treated as a vector. After that, an orthogonal clustering method is developed for HFSs. The main idea of this clustering method is that the orthogonal vectors in the distance matrix should be clustered into the same group, and according to the different values ofλ, the procedure will repeat again and again until all the cases are considered. Finally, two numerical examples are given to demonstrate the effectiveness of our algorithm.

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Authors and Affiliations

  1. Institute of Sciences, PLA University of Science and Technology, 211101, Nanjing, Jiangsu, China

    Yanmin Liu & Hua Zhao

  2. Business School, Sichuan University, 610064, Chengdu, Sichuan, China

    Zeshui Xu

Authors
  1. Yanmin Liu

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  2. Hua Zhao

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  3. Zeshui Xu

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Corresponding author

Correspondence toYanmin Liu.

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Liu, Y., Zhao, H. & Xu, Z. An orthogonal clustering method under hesitant fuzzy environment.Int J Comput Intell Syst10, 663–676 (2017). https://doi.org/10.2991/ijcis.2017.10.1.44

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