Model-Based Clustering Methods

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.