MATLAB Implementation of Fuzzy C-Means Algorithm with GUI Interface

This project implements the Fuzzy C-Means algorithm using MATLAB, complete with a GUI interface design for fuzzy clustering functionality. The FCM algorithm is a clustering technique based on fuzzy logic that partitions datasets into distinct categories while optimizing cluster centers for each category. Our MATLAB implementation includes key algorithmic components such as membership function calculation using Euclidean distance metrics, iterative centroid updates through weighted averaging, and convergence checking with tolerance thresholds. The GUI interface provides intuitive controls for dataset input parameters, cluster number specification, and visualization of clustering results through scatter plots and membership matrices. Users can interactively adjust fuzziness exponents and termination criteria to observe real-time clustering effects. Through this project, we demonstrate practical applications of fuzzy clustering principles while providing hands-on experience with MATLAB's App Designer for GUI development and matrix operations for efficient algorithm computation. This implementation serves as both an educational resource for understanding fuzzy clustering mechanics and a practical tool for solving real-world data classification problems.