Publicação
Probabilistic clustering of interval data
| Resumo: | In this paper we address the problem of clustering interval data, adopting a model-based approach. To this purpose, parametric models for interval-valued variables are used which consider configurations for the variance-covariance matrix that take the nature of the interval data directly into account. Results, both on synthetic and empirical data, clearly show the well-founding of the proposed approach. The method succeeds in finding parsimonious heterocedastic models which is a critical feature in many applications. Furthermore, the analysis of the different data sets made clear the need to explicitly consider the intrinsic variability present in interval data. |
|---|---|
| Autores principais: | Brito, Paula |
| Outros Autores: | Silva, A. Pedro Duarte; Dias, José G. |
| Assunto: | Clustering methods Finite mixture models Interval-valued variable Intrinsic variability Symbolic data |
| Ano: | 2015 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Católica Portuguesa |
| Idioma: | inglês |
| Origem: | Veritati - Repositório Institucional da Universidade Católica Portuguesa |
| Resumo: | In this paper we address the problem of clustering interval data, adopting a model-based approach. To this purpose, parametric models for interval-valued variables are used which consider configurations for the variance-covariance matrix that take the nature of the interval data directly into account. Results, both on synthetic and empirical data, clearly show the well-founding of the proposed approach. The method succeeds in finding parsimonious heterocedastic models which is a critical feature in many applications. Furthermore, the analysis of the different data sets made clear the need to explicitly consider the intrinsic variability present in interval data. |
|---|