Clustering: Understanding Intra-Class Similarity

This lecture delves into the concept of clustering, focusing on features and metrics to identify similar subgroups of pictures. The instructor discusses the importance of high intra-class similarity for effective clustering, illustrated through examples and interactive exercises. Various clustering methods such as K-means and DBSCAN are explored, along with the impact of different norms on clustering accuracy. The lecture also covers the projection onto principal components after PCA and the use of metrics like AIC, BIC, and RSS to evaluate clustering performance. Emphasis is placed on choosing the most cost-effective clustering method based on computational efficiency.