Probabilistic clustering of interval data

• Autor(a) principal: Brito, Paula
• Data de Publicação: 2015
• Outros Autores: Silva, A. Pedro Duarte, Dias, José G.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.14/20309
• topic: Clustering methods; Finite mixture models; Interval-valued variable; Intrinsic variability; Symbolic data

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.