Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.21/5005
Título: Enhancing the selection of a model-based clustering with external categorical variables
Autor: Baudry, Jean-Patrick
Cardoso, Margarida G. M. S.
Celeux, Gilles
Amorim, Maria José de Pina da Cruz
Ferreira, Ana Sousa
Palavras-chave: BIC
Categorical Variables
Mixed Type Variables Clustering
Mixture Models
Model-Based Clustering
Number of Clusters
Penalised Criteria
Data: Jun-2014
Editora: Springer-Verlag Berlin Heidelberg
Citação: BAUDRY, Jean-Patrick; [et al] – Enhancing the selection of a model-based clustering with external categorical variables. Advances in Data Analysis and Classification. ISSN: 1862-5347. (2014)
Resumo: In cluster analysis, it can be useful to interpret the partition built from the data in the light of external categorical variables which are not directly involved to cluster the data. An approach is proposed in the model-based clustering context to select a number of clusters which both fits the data well and takes advantage of the potential illustrative ability of the external variables. This approach makes use of the integrated joint likelihood of the data and the partitions at hand, namely the model-based partition and the partitions associated to the external variables. It is noteworthy that each mixture model is fitted by the maximum likelihood methodology to the data, excluding the external variables which are used to select a relevant mixture model only. Numerical experiments illustrate the promising behaviour of the derived criterion. © 2014 Springer-Verlag Berlin Heidelberg.
Peer review: yes
URI: http://hdl.handle.net/10400.21/5005
DOI: 10.1007/s11634-014-0177-3
ISSN: 1862-5347
Versão do Editor: http://link.springer.com/article/10.1007%2Fs11634-014-0177-3
Aparece nas colecções:ISEL - Matemática - Artigos

FacebookTwitterDeliciousLinkedInDiggGoogle BookmarksMySpace
Formato BibTex MendeleyEndnote Degois 

Todos os registos no repositório estão protegidos por leis de copyright, com todos os direitos reservados.