Fuzzy C-Means Clustering Algorithm Implementation in MATLAB

Implementation of the Fuzzy C-Means clustering algorithm in MATLAB, which serves as a powerful method for data clustering. This algorithm effectively groups data points based on their similarity characteristics and identifies underlying patterns within datasets. It handles data uncertainty through fuzzy membership degrees assigned to each data point, and employs an iterative optimization process to adjust cluster center positions for improved clustering performance. The implementation typically involves key functions such as initializing cluster centers, calculating membership matrices using distance metrics, and updating centroids through weighted averages. The Fuzzy C-Means clustering algorithm finds extensive applications across various domains including image processing, pattern recognition, and data mining. MATLAB's built-in functions and matrix operations make it particularly suitable for implementing this algorithm efficiently, with capabilities for visualizing clustering results and evaluating performance metrics.