K-Means Clustering Algorithm Implementation in MATLAB

This article explores the implementation of k-means clustering algorithm in MATLAB for efficient classification of large datasets. K-means clustering is a fundamental unsupervised learning algorithm that partitions datasets into k distinct clusters, maximizing similarity within clusters while minimizing similarity between different clusters. The algorithm implementation involves several key MATLAB functions including kmeans() for core clustering operations, pdist() for distance calculations, and silhouette() for cluster validation. Users can optimize classification results by adjusting parameters such as initial centroid selection methods ('plus' for k-means++ initialization), number of clusters, and distance metrics (Euclidean, Manhattan, or cosine). The algorithm employs an iterative process of assignment and centroid update until convergence, making it particularly valuable for applications in data mining, image segmentation, and natural language processing. MATLAB's vectorized operations ensure efficient handling of large-scale datasets through matrix computations.