Kernel K-Means Method

This lecture covers the kernel k-means method, which aims to avoid suboptimal solutions by initializing centroids to maximize their dispersion among the data. It introduces the concept of kernels to describe data in non-Euclidean spaces, allowing the formation of non-convex clusters. The lecture explains the derivation of the kernel k-means algorithm, highlighting the calculation of distances between observations and centroids. It also discusses the application of support vector machines (SVM) in non-linear problems through data redescription in Hilbert spaces. Additionally, the lecture explores clustering by density, emphasizing the identification of dense regions in datasets without predefined labels.