Distance for Dual Hesitant Fuzzy Granular Structures and Its Application in Clustering Algorithm
Yanxia Wei, Qinghai Wang · 2022
Compared with intuitionistic fuzzy sets and hesitant fuzzy sets, dual hesitant fuzzy sets can deal with more complex practical problems with their own advantages. At the same time, granular computing can abstract the real world from the granular aspect. Therefore, dual hesitant fuzzy set theory combined with granular computing can model practical problems more carefully and comprehensively. In this paper, we propose the distance for dual hesitant fuzzy granular structures, based on which the corresponding similarity measures can be obtained. At the same time, some mathematical properties of distance for dual hesitant fuzzy granular structures are proved. Finally, we apply this distance to the clustering algorithm of dual hesitant fuzzy granular structures. Through a numerical example, we find that the clustering algorithm using this distance can effectively improve the intra-cluster similarity, which also proves the usefulness and effectiveness of this paper.