MATLAB Implementation of C-Means Clustering Algorithm

This implementation provides the C-means clustering algorithm source code in MATLAB, which processes input data through training and category classification settings to produce data clustering diagrams. The C-means algorithm (also known as K-means) is a fundamental unsupervised learning method that groups input data based on similarity measures, facilitating data understanding and analysis. Key implementation features include centroid initialization methods (like K-means++), iterative Lloyd's algorithm for minimizing within-cluster variance, and convergence criteria handling. The algorithm workflow involves: 1) Randomly initializing cluster centroids 2) Assigning data points to nearest centroids using Euclidean distance 3) Recomputing centroids based on current assignments 4) Iterating until centroid stabilization. Through training and cluster number specification, the algorithm generates visualization-ready clustering diagrams that reveal underlying data structures and patterns. Core MATLAB functions employed include pdist2 for distance calculations, kmeans for optimized clustering, and scatter plots for result visualization. C-means clustering demonstrates significant utility across data mining applications for customer segmentation, image processing for pixel categorization, and pattern recognition for feature grouping. The implementation includes elbow method support for optimal cluster determination and handles multidimensional datasets through principal component analysis integration.