An Overview over Clustering for Extremes

In this project we provide an overview over several clustering methods for extremes by shedding light on similarities and differences between the three methods of k-means clustering, k-principal components clusterming and spherical clustering which are applied to the (estimated) spectral measure of a multivariate extreme value distribution. The aim is also to provide some practical guidelines about the implementations of all three methods.