Overview
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.
Syllabus
Recording start
Lecture start
Announcements
k-means intro
k-means formally
Lloyd's algorithm
Lloyd's algorithm efficiency
Lloyd's algorithm other properties
Initializing centroids
k-medoids
Cluster validation
Silhouette analysis
Regularization
Rand index
Lecture ends
Taught by
UofU Data Science