  This paper focuses on the problem of hierarchical non-overlapping clustering
of a dataset. In such a clustering, each data item is associated with exactly
one leaf node and each internal node is associated with all the data items
stored in the sub-tree beneath it, so that each level of the hierarchy
corresponds to a partition of the dataset. We develop a novel Bayesian
nonparametric method combining the nested Chinese Restaurant Process (nCRP) and
the Hierarchical Dirichlet Process (HDP). Compared with other existing Bayesian
approaches, our solution tackles data with complex latent mixture features
which has not been previously explored in the literature. We discuss the
details of the model and the inference procedure. Furthermore, experiments on
three datasets show that our method achieves solid empirical results in
comparison with existing algorithms.
