Lecture
Model-Based Clustering Methods
Description
This lecture covers model-based clustering methods, where data points are viewed as realizations of a mixture model with components. The lecture explains the concept of mixture models, probability distributions, latent variables, complete data likelihood, EM algorithm, family of models, number of clusters estimation, and model-based clustering in R using the example of iris data.
This video is available exclusively on Mediaspace for a restricted audience. Please log in to MediaSpace to access it if you have the necessary permissions.
Watch on MediaspaceOfficial source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.