Intuitionistic fuzzy statistical correlation algorithm with applications to multicriteria-based decision-making processes

Intuitionistic fuzzy set is a significance soft computing tool for curbing fuzziness embedded in decision-making processes. To enhance the applicability of intuitionistic fuzzy sets in modelling practical real-life problems, various computing methods have been proposed like distance measures, similarity measures and correlation measures. This paper proposes an intuitionistic fuzzy statistical correlation algorithm with applications to pattern recognition and diagnostic processes. This novel method assesses the magnitude of relationship and indicates whether the intuitionistic fuzzy sets under consideration are correlated in either positive or negative sense. We substantiate the proposed technique with some theoretical results and numerically validate it to be superior in terms of accuracy and reliability in contrast to some hitherto techniques. Finally, we determine decision-making processes involving pattern recognition and diagnostic processes by using JAVA programming language to code the intuitionistic fuzzy statistical correlation measure.