Evolutionary clustering for categorical data using parametric links among multinomial mixture models

Md Abul Hasnat 1 Julien Velcin 1 Stephane Bonnevay 1 Julien Jacques 2, 1
2 MODAL - MOdel for Data Analysis and Learning
Inria Lille - Nord Europe, LPP - Laboratoire Paul Painlevé - UMR 8524, CERIM - Santé publique : épidémiologie et qualité des soins-EA 2694, Polytech Lille, Université de Lille 1, IUT’A
Abstract : In this paper, we propose a novel evolutionary clustering method for temporal categorical data based on parametric links among multinomial mixture models. Besides clustering, our main goal is to interpret the evolutions of clusters over time. To this aim, first we propose the formulation of a generalized model that establishes parametric links among two multinomial mixture. Afterward, different parametric sub-models are defined in order to model typical evolutions of the clustering structure. Model selection criteria allow to select the best sub-models and thus to guess the clustering evolution. For the experiments, first we evaluate the proposed method with synthetic temporal data. Next, we apply it to analyze the annotated social media data. Results show that the proposed method is better than the state-of-the-art based on the common evaluation metrics. Additionally, it can provide interpretation about the temporal evolution of the clusters.
Type de document :
Pré-publication, Document de travail
2016
Liste complète des métadonnées

Littérature citée [40 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01204613
Contributeur : Julien Jacques <>
Soumis le : lundi 21 mars 2016 - 09:59:17
Dernière modification le : jeudi 11 janvier 2018 - 06:23:18
Document(s) archivé(s) le : mercredi 22 juin 2016 - 10:25:58

Fichier

PLMM.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01204613, version 2

Collections

Citation

Md Abul Hasnat, Julien Velcin, Stephane Bonnevay, Julien Jacques. Evolutionary clustering for categorical data using parametric links among multinomial mixture models. 2016. 〈hal-01204613v2〉

Partager

Métriques

Consultations de la notice

287

Téléchargements de fichiers

146