A Comparative Study of Clustering Methods with Multinomial Distribution

In this paper, we study different discrete data clustering methods which employ the Model-Based Clustering (MBC) with the Multinomial distribution. 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 that should be used for different sub-tasks for unsupervised discrete data analysis with the MBC method.