Clustering Methods

This lecture covers clustering methods, focusing on partitioning data points into meaningful classes when the labeling is unknown. It discusses the Swiss Fertility and Socioeconomic Indicators dataset, similarity-based clustering, dissimilarity measures, K-means algorithm, and hierarchical clustering. The instructor explains the bias-variance tradeoff, initialization issues in K-means, and different linkage clustering methods.