K-MEANS Algorithm Implementation in MATLAB Environment

This document provides comprehensive details about the K-MEANS algorithm implementation in MATLAB environment. K-MEANS is a centroid-based clustering algorithm that partitions datasets into multiple distinct clusters, widely applied in image processing, speech recognition, natural language processing, and other data analysis domains. The algorithm operates by iteratively assigning data points to the nearest cluster center (centroid) and recalculating centroids until convergence criteria are met.

The implementation requires MATLAB environment, and we provide a step-by-step tutorial covering environment setup to complete algorithm execution. Key MATLAB functions involved include kmeans() for core clustering, pdist() for distance calculations, and silhouette() for cluster validation. We also demonstrate methods for optimal K-value selection using elbow method or gap statistics, along with performance evaluation techniques such as cluster cohesion and separation metrics.

This documentation offers sufficient guidance for implementing K-MEANS clustering efficiently, including code snippets for data preprocessing, centroid initialization strategies (like k-means++), and visualization of clustering results using MATLAB's plotting capabilities.