Enhancing the selection of a model-based clustering with external categorical variables

• Autor(a) principal: Baudry, J.-P.
• Data de Publicação: 2015
• Outros Autores: Cardoso, M. G. M. S., Celeux, G., Amorim, M. J., Ferreira, A. S.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10071/9470
• topic: Mixture models; Model-based clustering; Number of clusters; Penalised criteria; Categorical variables; BIC; ICL; Mixed type variables clustering

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.