Clustering Algorithms

KPCA (Kernel Principle Component Analysis): Kernel principal component analysis (kernel PCA) is an extension of principal component analysis (PCA) using techniques of kernel methods. Using a kernel, the originally linear operations of PCA are done in a reproducing kernel Hilbert space with a non-linear mapping. Kernel PCA has been demonstrated to be useful for novelty detection and image de-noising

K-means Clustering: K-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean.