Clustering Evaluation

This lecture covers the evaluation of clustering algorithms using the RAND index, which measures the similarity between the ground truth labels and the clusters found. It also discusses the use of ontologies in clustering, where structured vocabularies help ensure consistent classification. By comparing clustering results to ontological statistics, probabilistic measures can assess the quality of the clustering. The lecture concludes by introducing classic clustering algorithms like hierarchical, centroid-based, and density-based clustering, which aim to optimize homogeneity and separability criteria through approximate methods.