Fuzzy C-Means Clustering: MATLAB Implementation with Complete Function Suite

This documentation presents a complete MATLAB implementation of Fuzzy C-Means (FCM) clustering algorithm. The package includes 10 specialized MATLAB functions that collectively implement the full FCM clustering workflow. Fuzzy C-Means clustering is a widely-used soft clustering algorithm that partitions datasets into overlapping clusters based on membership probabilities. The implementation handles key algorithmic components including membership matrix initialization, centroid calculation through iterative optimization, and convergence criteria checking. These MATLAB functions provide comprehensive capabilities for data preprocessing, cluster computation with customizable parameters (number of clusters, fuzzifier exponent), and result visualization through cluster plots and membership degree displays. The modular design allows users to easily adapt the algorithm for different datasets while maintaining the mathematical foundation of minimizing the weighted within-cluster sum of squares. Through these functions, researchers can efficiently perform clustering analysis, determine optimal cluster configurations, and derive meaningful insights from complex datasets.