Abstract：Traditional fuzzy methods are unable to solve the uncertainty of the data,but the hesitant fuzzy set methods can work efficiently.Existing hesitant fuzzy hierarchical clustering algorithms lack a reasonable weight calculation method,and the time complexity and space complexity of the algorithms are both exponential.In order to address these issues,this paper proposes a fuzzy hierarchical clustering algorithm with constant hesitation of agglomeration center(FHCA).Firstly,a weight formula based on the information of the data set is designed to obtain a more reasonable weight distribution.In addition,a new formula for calculating the cluster center is proposed,which not only makes the cluster center's hesitancy invariant during the clustering process,but also reduces the time complexity and space complexity of the original algorithm from exponential to linear,and the quality of clustering is not inferior to the original clustering algorithm.
2021, Vol. 42 
