Simultaneous Clustering and Model Selection for Multinomial Distribution: A Comparative Study

Md Abul Hasnat (1) , Julien Velcin (1) , Stéphane Bonnevay (1) , Julien Jacques (1, 2)

1 ERIC - Equipe de Recherche en Ingénierie des Connaissances
2 MODAL - MOdel for Data Analysis and Learning

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.

Domaines: Statistiques [math.ST]; Apprentissage [cs.LG]

Md Abul Hasnat, Julien Velcin, Stéphane Bonnevay, Julien Jacques. Simultaneous Clustering and Model Selection for Multinomial Distribution: A Comparative Study. Intelligent Data Analysis, Oct 2015, Saint Etienne, France. ⟨hal-01203561⟩