Clustering Algorithms: K-Means vs Spectral Clustering

This lecture covers two main clustering algorithms: K-Means and Spectral Clustering. K-Means is a popular method for partitioning data into clusters based on Euclidean distance, while Spectral Clustering is more flexible and does not assume specific cluster shapes. The lecture discusses the importance of choosing the right number of clusters and the optimal initialization for K-Means, as well as the selection of the similarity measure and number of clusters for Spectral Clustering. Practical examples include clustering students based on their effort and proactivity in a flipped classroom setting.