K-Means Clustering Algorithm Source Code (MATLAB Implementation)

This article presents the complete source code implementation of the K-means clustering algorithm. K-means is a fundamental machine learning algorithm used for partitioning data into distinct groups or clusters. As an unsupervised learning approach, the algorithm autonomously discovers patterns and groupings without requiring pre-labeled data or category information. Our implementation includes a fully functional MATLAB codebase that demonstrates the core algorithm workflow: initialization of cluster centroids, iterative assignment of data points to nearest centroids using Euclidean distance calculations, and updating centroid positions based on mean values of assigned points. The code features comprehensive comments explaining each computational step, including the convergence criteria handling and distance matrix computations. We provide practical usage examples demonstrating how to apply the algorithm to various datasets, along with performance optimization tips for handling large-scale data. This resource aims to help learners understand the algorithmic mechanics while providing a robust foundation for solving real-world data clustering challenges.