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A robust correlation coefficient for fermatean fuzzy sets based on spearman’s correlation measure with application to clustering and selection process

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Abstract

Fermatean fuzzy set (FFS) is an advance variant of fuzzy set applicable in curbing uncertainties and vagueness in complex decision making scenarios. Correlation coefficient is a measure of relationship between two variables or data with a significant importance in decision-making involving different areas of human endeavours. Several methods for calculating the correlation coefficient between FFSs have been developed and used to discuss sundry real-world problems. Albeit, these existing methods lack reliability, precision and give mispleading interpretations during real-life applications. Due to these setbacks, this work constructs a new method of Fermatean fuzzy correlation coefficient (FFCC) with practicable application potentials. This new method of FFCC is demonstrated to show accuracy, reliability, and superiority over the existing methods of FFCC through comparative studies. More so, the new method is applied to determine students’ academic performance via clustering analysis and selection process. This new method of FFCC could be used to discuss technique for order preference by similarity to ideal solutions and multiple attributes decision-making.

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Authors and Affiliations

  1. Department of Mathematics, Joseph Sarwuan Tarka University, Makurdi, Nigeria

    Paul Augustine Ejegwa, Tidoo Daniel Wanzenke, Manasseh Terna Anum & Kenneth Ifeanyi Isife

  2. Department of Mathematics and Computer Science, Benue State University, Makurdi, Nigeria

    Paul Augustine Ejegwa & Innocent Otache Ogwuche

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  1. Paul Augustine Ejegwa
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  2. Tidoo Daniel Wanzenke
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  3. Innocent Otache Ogwuche
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  4. Manasseh Terna Anum
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  5. Kenneth Ifeanyi Isife
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Correspondence to Paul Augustine Ejegwa.

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Ejegwa, P.A., Wanzenke, T.D., Ogwuche, I.O. et al. A robust correlation coefficient for fermatean fuzzy sets based on spearman’s correlation measure with application to clustering and selection process. J. Appl. Math. Comput. 70, 1747–1770 (2024). https://doi.org/10.1007/s12190-024-02019-1

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