MATLAB Implementation of K-Means Clustering Algorithm

In this article, we present a comprehensive MATLAB implementation of the k-means clustering algorithm that can be executed directly. Before demonstrating how to run the program, let's briefly review the k-means algorithm fundamentals. K-means is a widely-used clustering algorithm that partitions datasets into k distinct clusters, where data points within each cluster are highly similar while being distinctly different from points in other clusters. Our implementation includes several key features: - Core k-means algorithm with centroid initialization and iterative optimization - Multiple clustering evaluation metrics including silhouette coefficient and Davies-Bouldin index to quantitatively assess clustering quality - Visualization tools for better interpretation of clustering results through scatter plots and cluster boundary displays The code structure follows MATLAB best practices with modular functions for data preprocessing, cluster assignment, centroid calculation, and convergence checking. The main script handles parameter initialization and coordinates the complete clustering workflow. Users can easily modify parameters such as the number of clusters (k), maximum iterations, and distance metrics. We hope this implementation provides valuable assistance for your research or learning activities in data clustering and pattern recognition. The program is particularly useful for educational purposes and quick prototyping of clustering applications.