Novel Distance Measure for Hesitant Fuzzy Sets and Its Application to K-Means Clustering

Feng Yan, Xiaoqiang Zhou, Yongzhi Wang, Li Chen and Wu Li
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Feng Yan: Hunan Institute of Science and Technology, China
Xiaoqiang Zhou: Hunan Institute of Science and Technology, China
Yongzhi Wang: Hunan Institute of Science and Technology, China
Li Chen: Hunan Institute of Science and Technology, China
Wu Li: Hunan Institute of Science and Technology, China

International Journal of Fuzzy System Applications (IJFSA), 2022, vol. 11, issue 1, 1-32

Abstract: Distance measures have recently been studied in-depth within the context of hesitant fuzzy sets. The authors analyze existing research on the distance measures of hesitant fuzzy sets and identify several limitations. This paper proposes a new distance measure for hesitant fuzzy sets to overcome these shortcomings. First, a new hesitance degree with better accuracy and applicability is defined. Then, a new method for measuring the distance between hesitant fuzzy sets is proposed by considering the hesitance degree. On this basis, an improved hesitant fuzzy K-means clustering algorithm is introduced to classify hesitant fuzzy sets. Finally, an example is given to illustrate the specific implementation process of the clustering method, and a comparative study on the example is conducted.

Date: 2022
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