Exponential Function-Based Similarity Measures for q-Rung Linear Diophantine Fuzzy Sets and Their Application to Clustering Problem

Abstract

The q-rung linear Diophantine fuzzy set is a recently developed tool to handle with uncertain and awkward information in real-life issues and is applicable where reference parameter-based opinions. Similarity measures are distance with dimensions representing features of the objects. Keeping the advantages of the above analysis, this paper proposes similarity measures based on exponential function for q-rung linear Diophantine fuzzy sets and thus presents the first formulas for calculating the similarity coefficient between two q-rung linear Diophantine fuzzy sets. These proposed similarity measures are applied to the clustering problem and the results are analyzed. In addition, the comparison outputs of the new similarity measures are discussed to ensure their good performance.