Machine Learning – Unit V: ClusteringFree

Complete Machine Learning Unit V notes covering Clustering and Unsupervised Learning. The material explains how clustering groups similar data points without labelled data and covers Hard Clustering, Soft Clustering, Hierarchical Clustering, K-Means Clustering, Expectation-Maximization (EM), Fuzzy Clustering, and Spectral Clustering.

Topics include Agglomerative and Divisive Hierarchical Clustering, Dendrograms, Single/Complete/Average/Centroid Linkage, K-Means initialization and assignment steps, centroid updates, K-Means challenges and applications, EM algorithm with E-Step and M-Step, latent variables, likelihood, MLE, posterior probability, convergence, and Fuzzy Clustering.

The unit also covers Spectral Clustering, including graph-based clustering, eigenvalues and eigenvectors, dimensionality reduction, advantages, limitations, and applications.