Simplified KMeans Clustering Algorithm Implementation

This article demonstrates how to implement data classification using a simplified version of the KMeans clustering algorithm. KMeans is a widely-used clustering algorithm that partitions datasets into distinct groups based on feature similarity. The implementation utilizes MATLAB's computational capabilities, featuring key functions like kmeans() for cluster assignment and centroid calculation. The algorithm workflow typically involves: initializing cluster centers randomly, assigning data points to nearest centroids using Euclidean distance, recalculating centroids based on mean values of assigned points, and iterating until convergence. Through this KMeans implementation, users can gain deeper insights into data distribution patterns and perform subsequent analysis and processing based on the identified clusters. Understanding the fundamental principles and implementation techniques of KMeans clustering is therefore essential for effective data analysis and machine learning applications. The MATLAB code structure includes parameters for specifying the number of clusters (k), maximum iterations, and distance metric selection, providing flexibility for various dataset characteristics.