A new kernel clustering algorithm

We propose a new kernel clustering algorithm. It estimates an in advance fixed number of vectors and margins in a feature space. Each pair of vector and margin defines a hyperplane in feature space and thus separates the data in two clusters. All the clusters together carry important information about the data set. The estimation in feature space is done implicitly by the use of a kernel. Therefore nonlinear clusters in the space of the data can be obtained. The clusters are estimated by optimizing a homogeneous quadratic program. We show how our algorithm can be efficiently implemented and we demonstrate the usefulness with a real world example.