This thesis aims to apply the Dirichlet process mixture model to the cluster kernel framework. The probabilistic cluster kernel is extended with a Bayesian nonparametric model to avoid critical parameters within the model. The Dirichlet process cluster kernel demonstrate advantages compared to the probabilistic cluster kernel in both classification and clustering. Additionally, the two dimensional ...