A New Divergence Measure for Intuitionistic Fuzzy Matrices

Data available in the real world may not be in a crisp format. Intuitionistic fuzzy matrices are applicable in uncertainty and useful in decision making, relational equation, clustering, etc. Divergence or similarity measures help to characterize dissimilarity or similarity between any two sets. This paper presents a new divergence measure for intuitionistic fuzzy matrices with the verification of its validity. The fundamental properties are demonstrated for the new intuitionistic fuzzy divergence measure. A technique to solve multi-criteria decision-making problems is developed by utilizing the proposed intuitionistic fuzzy divergence measure. Finally, application in the medical diagnosis of this intuitionistic fuzzy divergence measure to decision making is shown using real data.