A new intuitionistic fuzzy correlation coefficient approach with applications in multi-criteria decision-making

The Intuitionistic Fuzzy Set (IFS) is a powerful extension of Fuzzy Set (FS) used to handle vagueness and uncertainty. The correlation coefficient is an established tool for finding the strength of relationships between two IFSs. This study proposes a new approach for finding a correlation coefficient to compare with the existing approach and show its superiority over the existing approach. In many counter-intuitive cases, existing approaches fail to handle the problem appropriately by giving indeterminate form, incorrect and inaccurate results. We demonstrate the reliability and efficiency of the proposed approach in handling these concerns. We also present the application of the proposed approach in pattern detection, medical illness diagnosis, clustering, and Multi-Criteria Decision-Making (MCDM) to further exhibit its practicability and effectiveness.