# Inferring Hierarchical Mixture Structures: A Bayesian Nonparametric Approach

## TL;DR

This paper introduces a new Bayesian nonparametric method combining nCRP and HDP for hierarchical clustering, effectively handling complex latent mixture data structures.

## Contribution

It presents a novel approach that integrates nCRP and HDP to address hierarchical clustering of complex mixture data, a problem not previously tackled.

## Key findings

- Achieves superior clustering accuracy on multiple datasets.
- Effectively models complex latent mixture structures.
- Outperforms existing Bayesian hierarchical clustering methods.

## Abstract

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.