Data Clustering Lecture
Nowadays, Artificial Intelligence drives scientific and economic growth worldwide. This is largely due to advances in Machine Learning (ML). Its applications span and revolutionize almost every human activity:
-Autonomous Systems (cars, drones, vessels),
-Media Content and Art Creation (including fake data creation/detection), Social Media Analytics,
-Medical Imaging and Diagnosis,
-Financial Engineering (forecasting and analytics), Big Data Analytics,
-Broadcasting, Internet and Communications,
-Robotics/Control
-Intelligent Human-Machine Interaction, Anthropocentric (human-centered) Computing,
-Smart Cities/Buildings and Assisted living.
-Scientific Modeling and Analytics.
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.