K-Medoids Clustering Algorithm Source Code with MATLAB Implementation

This MATLAB routine provides a straightforward and easily comprehensible implementation of the k-medoids clustering algorithm. The implementation begins with data initialization and selection of k initial points as cluster centers. The algorithm then computes the distance between each data point and all cluster centers, assigning each point to its nearest centroid. A key implementation detail involves the update step where cluster centers are recalculated as the medoids - the actual data points that minimize the sum of distances to all other points within the same cluster, rather than using mean values like k-means. This medoid selection typically employs a pairwise distance comparison approach within each cluster. The process iterates until cluster centers stabilize and no further changes occur. This routine serves as an excellent educational tool for understanding the fundamental workings of the k-medoids algorithm and provides a solid foundation for custom implementations, demonstrating practical aspects like distance matrix computation, cluster assignment logic, and convergence checking mechanisms.