K-means Algorithm Implementation with MATLAB Code and Detailed Explanations

This documentation presents a MATLAB implementation of the K-means algorithm accompanied by detailed explanations. K-means is a clustering algorithm that partitions data into K distinct clusters by minimizing the within-cluster sum of squared errors. This algorithm is commonly applied in unsupervised learning, data mining, and image segmentation applications. When implementing the K-means algorithm, several critical factors must be considered: the selection of optimal cluster number K, the randomness in centroid initialization, and the number of iterations required for convergence. The algorithm typically involves key MATLAB functions such as kmeans() for core clustering, pdist() for distance calculations, and squareform() for matrix conversions. The initialization process often uses random centroid selection or the k-means++ method for better convergence. The iterative process involves assigning data points to nearest centroids using Euclidean distance and recalculating centroids based on cluster means. Convergence criteria may include maximum iteration limits or centroid movement thresholds. This documentation provides extensive guidance to help beginners understand the algorithm's mathematical foundations and practical implementation aspects, including code optimization techniques and common parameter tuning approaches for different dataset characteristics.