K-means MATLAB Source Code for Cluster Analysis

This document provides a MATLAB source code implementation of the K-means clustering algorithm designed for cluster analysis applications. The program performs clustering by grouping data points into K distinct clusters based on feature similarity, minimizing within-cluster variances through an iterative optimization process. Key implementation features include: - Centroid initialization methods (random or k-means++ enhancement) - Configurable distance metrics (Euclidean, Manhattan, or cosine distance) - Iterative centroid updating and point reassignment until convergence - Maximum iteration limits and convergence tolerance controls Cluster analysis serves as a fundamental data mining technique for identifying patterns and trends within datasets. Users can customize algorithm parameters such as cluster count (K value), initialization method, and distance calculation approach to optimize clustering results for specific applications. The code includes error handling for empty clusters and provides cluster labeling for each data point. To maximize effectiveness, users should possess basic understanding of clustering concepts including centroid calculation, within-cluster sum of squares (WCSS), and the elbow method for determining optimal K values. The implementation outputs cluster centroids, assignment labels, and iteration statistics for result validation.