Unsupervised Learning: Clustering Methods

This lecture covers unsupervised learning, focusing on clustering methods such as K-means and DBSCAN. It explains the clustering problem, characteristics of clustering methods, use cases for clustering, and the challenges faced in clustering high-dimensional data. The instructor discusses hierarchical clustering, flat clustering, and the implementation of clustering algorithms. Additionally, the lecture delves into the concepts of cluster bias, cluster shapes, and the difficulties in choosing the number of clusters. Various clustering algorithms are explored, including K-means and DBSCAN, highlighting their properties, drawbacks, and performance. The lecture concludes with a detailed explanation of DBSCAN, its algorithm, performance, and the challenges it addresses in clustering applications.