Kernel K-means: Iterative Clustering Algorithm

This lecture covers the Kernel K-means algorithm, an iterative procedure involving cluster initialization, data point assignment to centroids, and cluster point list updates. The influence of terms in the clustering process, such as the RBF kernel, is discussed. The lecture also delves into interpreting the objective function and the impact of cluster density and point proximity on the algorithm's performance.