K-means Clustering Demonstration in MATLAB

This MATLAB demonstration provides a practical implementation of the K-means clustering algorithm, showcasing both command-line and graphical user interface (GUI) approaches. The code utilizes MATLAB's built-in kmeans function, which implements Lloyd's algorithm - an iterative refinement technique that partitions data into k clusters by minimizing within-cluster variances. The demonstration includes data preprocessing steps and parameter configuration for the kmeans function, allowing users to specify the number of clusters (k value) and convergence criteria. The algorithm works by initializing cluster centroids, assigning data points to nearest centroids, and iteratively updating centroids until stabilization. For result visualization, the code employs MATLAB's plotting functions: scatter plots using plot() function display cluster distributions, while gscatter() creates color-coded visualizations with cluster centroids marked distinctly. The implementation includes error handling for empty clusters and demonstrates how to assess clustering quality using within-cluster sum of squares metrics. This resource serves as an excellent educational tool for understanding unsupervised learning techniques, particularly for researchers and students working with pattern recognition and data mining applications. The code structure follows MATLAB best practices, making it easily adaptable for various datasets and clustering scenarios.