Similarity measures for complex Fermatean fuzzy sets and their applications in decision-making and clustering problems

The quantification of similarity between fuzzy objects is central to decision-making and clustering under uncertainty. While numerous measures have been developed within Intuitionistic, Pythagorean, and Fermatean fuzzy frameworks, these approaches cannot be directly applied to Complex Fermatean Fuzzy Sets (CFFSs) because CFFSs represent membership and nonmembership degrees through complex amplitudes and phases. This study proposes novel similarity measures specifically designed for CFFSs, establishes their fundamental mathematical properties, and demonstrates their performance through applications in decision-making and clustering. The results confirm that the proposed measures effectively capture complex-valued uncertainty and provide a reliable foundation for practical fuzzy analysis.

کلیدواژه ها:

Complex Fermatean fuzzy Sets ، Similarity measure ، decision-making ، Clustering