Simultaneous Clustering and Model Selection for Multinomial Distribution: A Comparative Study
Abstract
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.
Cite
@article{arxiv.1505.02324,
title = {Simultaneous Clustering and Model Selection for Multinomial Distribution: A Comparative Study},
author = {Md. Abul Hasnat and Julien Velcin and Stéphane Bonnevay and Julien Jacques},
journal= {arXiv preprint arXiv:1505.02324},
year = {2015}
}
Comments
Accepted in the International Symposium on Intelligent Data Analysis (IDA 2015)