Cargando…

Novel pruning and truncating of the mixture of vine copula clustering models

The mixture of the vine copula densities allows selecting the vine structure, the most appropriate type of parametric marginal distributions, and the pair-copulas individually for each cluster. Therefore, complex hidden dependence structures can be fully uncovered and captured by the mixture of vine...

Descripción completa

Detalles Bibliográficos
Autor principal: Alanazi, Fadhah Amer
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9671921/
https://www.ncbi.nlm.nih.gov/pubmed/36396705
http://dx.doi.org/10.1038/s41598-022-24274-7
_version_ 1784832644980146176
author Alanazi, Fadhah Amer
author_facet Alanazi, Fadhah Amer
author_sort Alanazi, Fadhah Amer
collection PubMed
description The mixture of the vine copula densities allows selecting the vine structure, the most appropriate type of parametric marginal distributions, and the pair-copulas individually for each cluster. Therefore, complex hidden dependence structures can be fully uncovered and captured by the mixture of vine copula models without restriction to the parametric shape of margins or dependency patterns. However, this flexibility comes with the cost of dramatic increases in the number of model parameters as the dimension increases. Pruning and truncating each cluster of the mixture model will dramatically reduce the number of model parameters. This paper, therefore, introduced the first pruning and truncating techniques for the model-based clustering algorithm using the vine copula model, providing a significant contribution to the state-of-the-art. We apply the proposed methods to a number of well-known data sets with different dimensions. The results show that the performance of the individual pruning and truncation for each model cluster is superior to an existing vine copula clustering model.
format Online
Article
Text
id pubmed-9671921
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-96719212022-11-19 Novel pruning and truncating of the mixture of vine copula clustering models Alanazi, Fadhah Amer Sci Rep Article The mixture of the vine copula densities allows selecting the vine structure, the most appropriate type of parametric marginal distributions, and the pair-copulas individually for each cluster. Therefore, complex hidden dependence structures can be fully uncovered and captured by the mixture of vine copula models without restriction to the parametric shape of margins or dependency patterns. However, this flexibility comes with the cost of dramatic increases in the number of model parameters as the dimension increases. Pruning and truncating each cluster of the mixture model will dramatically reduce the number of model parameters. This paper, therefore, introduced the first pruning and truncating techniques for the model-based clustering algorithm using the vine copula model, providing a significant contribution to the state-of-the-art. We apply the proposed methods to a number of well-known data sets with different dimensions. The results show that the performance of the individual pruning and truncation for each model cluster is superior to an existing vine copula clustering model. Nature Publishing Group UK 2022-11-17 /pmc/articles/PMC9671921/ /pubmed/36396705 http://dx.doi.org/10.1038/s41598-022-24274-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Alanazi, Fadhah Amer
Novel pruning and truncating of the mixture of vine copula clustering models
title Novel pruning and truncating of the mixture of vine copula clustering models
title_full Novel pruning and truncating of the mixture of vine copula clustering models
title_fullStr Novel pruning and truncating of the mixture of vine copula clustering models
title_full_unstemmed Novel pruning and truncating of the mixture of vine copula clustering models
title_short Novel pruning and truncating of the mixture of vine copula clustering models
title_sort novel pruning and truncating of the mixture of vine copula clustering models
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9671921/
https://www.ncbi.nlm.nih.gov/pubmed/36396705
http://dx.doi.org/10.1038/s41598-022-24274-7
work_keys_str_mv AT alanazifadhahamer novelpruningandtruncatingofthemixtureofvinecopulaclusteringmodels