Lecture
This lecture covers the Kernel K-means algorithm, which extends the classical K-means clustering to generate non-linear separations of data points. It explains the algorithm steps, advantages, limitations, and the interpretation of the solution using different kernels. The instructor demonstrates how Kernel K-means can create nonlinear boundaries and provides insights into interpreting the solution with polynomial and RBF kernels.