Hesitant Fuzzy Linguistic Agglomerative Hierarchical Clustering Algorithm and Its Application in Judicial Practice

Abstract

The fuzzy clustering algorithm has become a research hotspot in many fields because of its better clustering effect and data expression ability. However, little research focuses on the clustering of hesitant fuzzy linguistic term sets (HFLTSs). To fill in the research gaps, we extend the data type of clustering to hesitant fuzzy linguistic information. A kind of hesitant fuzzy linguistic agglomerative hierarchical clustering algorithm is proposed. Furthermore, we propose a hesitant fuzzy linguistic Boole matrix clustering algorithm and compare the two clustering algorithms. The proposed clustering algorithms are applied in the field of judicial execution, which provides decision support for the executive judge to determine the focus of the investigation and the control. A clustering example verifies the clustering algorithm’s effectiveness in the context of hesitant fuzzy linguistic decision information.

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“…It starts by dividing each piece of data into a complete cluster. Then, according to the similarity of the data, the nearest clusters are gradually merged into a cluster until all clusters have merged into one cluster or some established conditions have been reached [ 5 ]. The K-means algorithm, density-based spatial clustering of applications with noise algorithm, and ordering points to identify the clustering structure algorithm of the AHC algorithm are preferred algorithms in the analysis methods [ 6 , 7 ].…”