In this paper we consider the clustering of text documents using the Chinese Restau- rant Process (CRP) and extensions that take time-correlations into account. To this pur- pose, we implement and test the Distance Dependent Chinese Restaurant Process (DD- CRP) for mixture models on both generated and real-world data. We also propose and im- plement a novel clustering algorithm, the Av- eraged Distance Dependent Chinese Restau- rant Process (ADDCRP), to model time- correlations, that is faster per iteration and attains similar performance as the fully dis- tance dependent CRP
