Evolutionary clustering for categorical data using parametric links among multinomial mixture models - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Econometrics and Statistics Year : 2017

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

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.
Fichier principal
Vignette du fichier
PLMM.pdf (2.3 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01204613 , version 1 (24-09-2015)
hal-01204613 , version 2 (21-03-2016)
hal-01204613 , version 3 (27-02-2019)

Identifiers

Cite

Md Abul Hasnat, Julien Velcin, Stephane Bonnevay, Julien Jacques. Evolutionary clustering for categorical data using parametric links among multinomial mixture models. Econometrics and Statistics , 2017, 3, pp.141-159. ⟨10.1016/j.ecosta.2017.03.004⟩. ⟨hal-01204613v3⟩
593 View
586 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More