This lecture overviews Data Clustering that has many applications in e.g., facial image clustering, signal/image clustering, concept creation. It covers the following topics in detail: Clustering Definitions. Distance measures, Mahalanobis distance, Euclidean distance, Lp norm, L1 Norm Similarity measures, Cosine similarity, Correlation coefficient. Distance Functions between a Point and a Set. Distance Functions between two Sets. Clustering algorithm categories: Exhaustive Clustering. Sequential Clustering, Maximin algorithm. Clustering by optimization, K-means algorithm, ISODATA algorithm. Fuzzy clustering. Vector Quantization, Voronoi regions, LVQs. Graph-based clustering, N-Cut Graph Clustering, Spectral graph clustering.
