Clustering Techniques: K-means and DBSCAN

This lecture covers the practical implementation of clustering techniques, focusing on k-means and DBSCAN algorithms. The instructor explains how to determine the number of clusters in k-means and the classification types in DBSCAN. The lecture includes hands-on exercises on assigning data points to centroids and computing new centroid positions. Additionally, it discusses the concept of core, border, and noise points in DBSCAN, illustrating how these points are classified and connected in a dataset.