MATLAB Implementation of C-Means Clustering Algorithm

This implementation provides a pure MATLAB code solution for C-means clustering algorithm developed following the step-by-step process described in pattern recognition textbooks. The program avoids using any built-in toolbox functions, offering a transparent view of the algorithm's core mechanisms. I have developed a MATLAB program that implements the C-means clustering algorithm based on the detailed procedures outlined in pattern recognition textbooks. This implementation operates independently of MATLAB's built-in toolboxes, ensuring a fundamental understanding of the algorithm's workings. The C-means clustering algorithm, also known as K-means clustering, is a widely-used unsupervised learning method that partitions data points into distinct clusters based on their feature similarities. During implementation, I meticulously followed the textbook's algorithmic steps: initial centroid selection, distance calculation using Euclidean metric, iterative assignment of data points to nearest centroids, and centroid recalculation until convergence. Key functions include data normalization routines, centroid initialization methods, and convergence criteria checking. The code demonstrates proper handling of cluster assignment updates and includes visualization components for result analysis. This approach provides educational insight into the algorithm's mathematical foundation while maintaining practical applicability for clustering tasks.