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A Survey of Fuzzy Clustering Methods and Validation Approaches

Publication Type : Conference Proceedings

Publisher : Springer Nature Singapore

Source : Lecture Notes in Networks and Systems

Url : https://doi.org/10.1007/978-981-96-2647-2_12

Campus : Bengaluru

School : School of Artificial Intelligence

Year : 2025

Abstract : This work presents a thorough and meticulous examination of fuzzy partition approaches, including a complete algorithmic description of the Fuzzy C-Means (FCM), Fuzzy J-Means (FJM), and Electrical Fuzzy C-Means (EFCM) clustering algorithms. It thoroughly examines these three algorithms on four datasets: iris, diabetes, heart disease, and breast cancer. The paper employs a wide range of fuzzy versions of validity measures and discusses internal and external fuzzy clustering validity measures to compare the performance of these algorithms. In conclusion, this paper offers well-founded suggestions on the performance of these three algorithms and discusses their effectiveness based on different numbers of clusters.

Cite this Research Publication : Pratik Singh Thakur, Sarada Mohapatra, A Survey of Fuzzy Clustering Methods and Validation Approaches, Lecture Notes in Networks and Systems, Springer Nature Singapore, 2025, https://doi.org/10.1007/978-981-96-2647-2_12

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