Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Learn about k-means clustering algorithms and validation techniques in this comprehensive lecture from the University of Utah's Data Science program. Explore fundamental concepts starting with an introduction to k-means, followed by formal mathematical definitions and detailed explanations of Lloyd's algorithm including its efficiency and properties. Discover various methods for initializing centroids and understand the k-medoids variation. Master cluster validation techniques through silhouette analysis, examine the role of regularization in clustering, and learn how to evaluate clustering results using the Rand index. The lecture provides both theoretical foundations and practical insights for implementing clustering algorithms in data mining applications.