Lecture
This lecture covers the concept of k-means clustering, where data points are assigned to clusters based on their proximity. The instructor explains the process step by step, from understanding the algorithm to minimizing the within-cluster sum of squared distances. The lecture also delves into the application of k-means in clustering data points, emphasizing the goal of grouping similar data points together. Additionally, the lecture touches upon the use of Euclidean distance in measuring proximity and the iterative nature of the k-means algorithm.