Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Learn about spectral clustering in this comprehensive lecture from the University of Utah's Data Science program. Explore fundamental concepts starting with an introduction to spectral clustering and graph theory, before diving into advanced topics like graph partitioning problems and Laplacian matrices. Master both unnormalized and normalized graph Laplacians through detailed explanations and practical examples. Understand eigenvalues and eigenvectors in the context of spectral clustering, and discover how to approximate the RatioCut problem. Examine similarity graphs and their applications, concluding with a thorough walkthrough of the spectral clustering algorithm. Perfect for data science students and practitioners looking to enhance their clustering analysis skills.