Fundamentals and Code Implementation of K-means Clustering Algorithm

This article introduces the fundamental concepts of the K-means clustering algorithm, a widely-used unsupervised learning method for data clustering. The algorithm efficiently partitions data into distinct clusters where each cluster contains data points with similar characteristics. We provide complete implementations of K-means in MATLAB, C, and C++ programming languages. The code demonstrates essential algorithmic components including centroid initialization methods, distance calculation using Euclidean or Manhattan metrics, cluster assignment procedures, and centroid update mechanisms. Key implementation details covered: - Random or k-means++ initialization for optimal starting points - Iterative distance computation between data points and centroids - Cluster reassignment based on minimum distance criteria - Convergence checking using centroid movement thresholds Through studying and modifying these implementations, you'll gain deeper understanding of K-means clustering and can adapt the algorithm for your specific datasets. The code structure allows for customization of distance metrics, initialization methods, and convergence criteria to suit various application requirements.