Evolutionary clustering for categorical ...
Document type :
Article dans une revue scientifique
Permalink :
Title :
Evolutionary clustering for categorical data using parametric links among multinomial mixture models
Author(s) :
Hasnat, Md Abul [Auteur]
Velcin, Julien [Auteur]
Bonnevay, Stéphane [Auteur]
Jacques, Julien [Auteur]
Velcin, Julien [Auteur]
Bonnevay, Stéphane [Auteur]
Jacques, Julien [Auteur]
Journal title :
Econometrics and Statistics
Volume number :
3
Pages :
141-159
Publisher :
Elsevier
Publication date :
2017-07
ISSN :
2452-3062
Keyword(s) :
Twitter data
Evolutionary clustering
Multinomial distribution
Mixture model
Model-based clustering
Evolutionary clustering
Multinomial distribution
Mixture model
Model-based clustering
HAL domain(s) :
Mathématiques [math]/Statistiques [math.ST]
English abstract : [en]
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 ...
Show more >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.Show less >
Show more >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.Show less >
Language :
Anglais
Audience :
Internationale
Popular science :
Non
Submission date :
2020-06-08T14:10:52Z
2020-06-09T09:27:53Z
2020-06-09T09:27:53Z
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