Simultaneous Clustering and Model Selection for Multinomial Distribution: A Comparative Study - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2015

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.
Fichier principal
Vignette du fichier
IDA_CR.pdf (756.92 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01203561 , version 1 (24-09-2015)

Identifiers

  • HAL Id : hal-01203561 , version 1

Cite

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⟩
148 View
571 Download

Share

Gmail Facebook X LinkedIn More