Mathematics and Computational Sciences

Similarity Measures for Complex Fermatean Fuzzy Sets and Their Applications in Decision-making and Clustering Problems

Articles in Press

Document Type : Original Article

Authors

Nisha Devi G 1
Hemavathi P 2
Vinod Kumar R 3
Suresh K 4

1 Research Scholar, Department of Mathematics, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha School of Engineering, Thandalam, India.

2 Assistant Professor (SG), Department of Mathematics, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha School of Engineering, Thandalam, India.

3 Assistant Professor, Department of Mathematics, Rajalakshmi Engineering College, Thandalam, Chennai-602 105.

4 Associate Professor, Department of Mathematics, St. Joseph College of Engineering, Chennai, India.

https://doi.org/10.30511/mcs.2026.2078363.1638
Abstract
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.

Keywords

Complex Fermatean fuzzy Sets
Similarity measure
Decision-making
Clustering
Subjects
Mathematics and Computational Sciences

Articles in Press, Accepted Manuscript
Available Online from 14 May 2026

  • Receive Date 19 November 2025
  • Revise Date 13 February 2026
  • Accept Date 14 May 2026

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