Source Code for Fuzzy C-Means Clustering and Fuzzy K-Means Clustering Algorithms

This document provides MATLAB source code implementations for both Fuzzy C-Means (FCM) and Fuzzy K-Means clustering algorithms. These practical implementations feature optimized convergence mechanisms and membership function calculations that significantly improve data processing accuracy. The FCM algorithm implements iterative centroid updates with fuzzy partitioning, while the Fuzzy K-Means variant incorporates enhanced cluster validity measures. Each program includes comprehensive documentation detailing key functions such as initialize_clusters(), calculate_membership(), and update_centroids(), along with parameter optimization guidelines. These implementations support customizable distance metrics and termination criteria, making them particularly valuable for pattern recognition and data mining applications. The accompanying documentation explains both the theoretical foundations and practical usage scenarios, ensuring effective implementation for various research and industrial applications.