K-means Algorithm Implementation in MATLAB

This documentation presents a MATLAB implementation of the k-means clustering algorithm, complete with test datasets for performance evaluation. The implementation includes core algorithmic components such as centroid initialization, distance calculation using Euclidean metrics, and iterative cluster assignment updates. Users can test the algorithm's performance on various datasets and observe convergence patterns through visualization tools. We recommend conducting deeper research to understand the algorithm's strengths and limitations across different data distributions, including sensitivity to initial centroid selection and handling of non-spherical clusters. For optimization considerations, users might explore elbow method for determining optimal k-values or implement k-means++ initialization for improved convergence. Additionally, we encourage comparative analysis with alternative clustering approaches like hierarchical clustering or DBSCAN to identify the most suitable algorithm for specific data characteristics. The code structure allows modular modifications for implementing silhouette analysis or other validation metrics. Finally, we advocate sharing research findings with the academic community to contribute to advancements in clustering methodologies and machine learning applications.