Clustering Methods: K-means and DBSCAN

This lecture covers clustering methods, focusing on K-means and DBSCAN algorithms. It explains the concepts of supervised and unsupervised machine learning, the clustering problem, characteristics of clustering methods, use cases for clustering, and the challenges of high-dimensional clustering. The instructor discusses the K-means algorithm, its properties, drawbacks, initialization, and how to choose the optimal number of clusters. Additionally, the lecture delves into the DBSCAN algorithm, its density-based approach, core points, density-reachable points, and the DBSCAN clustering process. The presentation concludes with a comparison of K-means and DBSCAN performance.