MATLAB Implementation of Clustering Algorithms with Multiple Distance Metrics

This article explores a practical implementation of clustering algorithms utilizing multiple distance metrics including absolute distance, Chebyshev distance, and mean distance. We present a comprehensive MATLAB program featuring a graphical user interface (GUI) that simplifies algorithm interaction and parameter configuration. The implementation demonstrates key clustering concepts through practical code examples, including distance matrix computation using pdist() function with custom metric handlers, centroid calculation routines, and iterative clustering procedures. Through detailed code walkthroughs, readers will understand the operational mechanisms of clustering algorithms and learn how to implement them efficiently in MATLAB. The discussion progresses systematically from fundamental clustering concepts to advanced implementation techniques, concluding with a complete overview of the MATLAB programming workflow. The GUI component integrates callback functions for real-time parameter adjustment and visualization capabilities through MATLAB's plotting functions, enabling users to observe clustering results dynamically.