Abstract
This article presents algorithm for \(c\)-means clustering under Pythagorean fuzzy environment. In this method Pythagorean fuzzy generator is developed to convert the data points from crisp to Pythagorean fuzzy numbers (PFNs). Subsequently, Euclidean distance is used to measure the distances between data points. From the viewpoint of capturing imprecise information, the proposed method is advantageous in the sense that associated PFNs not only consider membership and non-membership degrees, but also includes several other variants of fuzzy sets for information processing. The proposed algorithm is applied on synthetic, UCI and two moon datasets to verify the validity and efficiency of the developed method. Comparing the results with the fuzzy and intuitionistic fuzzy \(c\)-means clustering algorithms, the proposed method establishes its higher capability of partitioning data using Pythagorean fuzzy information.
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Acknowledgments
One author, Souvik Gayen, is grateful to UGC, Govt. of India, for providing financial support to execute the research work vide UGC Reference No. 1184/(SC). The suggestions and comments on the reviewers on this article are thankfully acknowledged by the authors.
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Gayen, S., Biswas, A. (2021). Pythagorean Fuzzy c-means Clustering Algorithm. In: Dutta, P., Mandal, J.K., Mukhopadhyay, S. (eds) Computational Intelligence in Communications and Business Analytics. CICBA 2021. Communications in Computer and Information Science, vol 1406. Springer, Cham. https://doi.org/10.1007/978-3-030-75529-4_10
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