  In this paper, we study different discrete data clustering methods, which use
the Model-Based Clustering (MBC) framework with the Multinomial distribution.
Our study comprises several relevant issues, such as initialization, model
estimation and model selection. Additionally, we propose a novel MBC method by
efficiently combining the partitional and hierarchical clustering techniques.
We conduct experiments on both synthetic and real data and evaluate the methods
using accuracy, stability and computation time. Our study identifies
appropriate strategies to be used for discrete data analysis with the MBC
methods. Moreover, our proposed method is very competitive w.r.t. clustering
accuracy and better w.r.t. stability and computation time.
