Divergence Measures and Aggregation Operators for Single-Valued Neutrosophic Sets with Applications in Decision-Making Problems

Single-valued neutrosophic sets (SVNSs) facilitate the representation of uncertain information more extensively than conventional methods. The study of divergence measures of SVNSs is important due to their applications in different areas like multi-criteria decision-making (MCDM), pattern recognition, cluster analysis, machine learning, etc., In this paper, we introduce a divergence measure for SVNSs. The suggested divergence measure is applied to cluster analysis for the classification of imprecise data. For establishing the reasonability and advantage of the suggested divergence measure in a clustering problem over the existing measures, a comparative assessment is also presented. Furthermore, we introduce, an inferior ratio method for handling the MCDM problem in the SVN environment. The consistency of the results of the suggested method with existing approaches also supports the credibility of its practical usage.