Title :

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

Author(s) :

Hasnat, Md Abul [Auteur]
Equipe de Recherche en Ingénierie des Connaissances [ERIC]
Velcin, Julien [Auteur]
Equipe de Recherche en Ingénierie des Connaissances [ERIC]
Bonnevay, Stéphane [Auteur]
Equipe de Recherche en Ingénierie des Connaissances [ERIC]
Jacques, Julien [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Equipe de Recherche en Ingénierie des Connaissances [ERIC]

Conference title :

Intelligent Data Analysis

City :

Saint Etienne

Country :

France

Start date of the conference :

2015-10-22

HAL domain(s) :

Mathématiques [math]/Statistiques [math.ST]
Informatique [cs]/Apprentissage [cs.LG]

English abstract : [en]

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, ...
Show more >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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

ANR Project :

Images sur le Web : analyse de la dynamique des images sur le Web 2.0.

Collections :

• Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL