Scholarlink

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.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Survey of Fuzzy Clustering Methods and Validation Approaches

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Pratik Singh Thakur,
  • Sarada Mohapatra

摘要

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.