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Orientador(es)
Resumo(s)
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.
Descrição
Palavras-chave
Clustering methods Finite mixture models Interval-valued variable Intrinsic variability Symbolic data
Contexto Educativo
Citação
BRITO, Paula; DUARTE SILVA, A. P.; DIAS, José G. - Probabilistic Clustering of Interval Data. Intelligent Data Analysis. 1571-4128. Vol. 19, n.º 2 (2015), p. 293-313
Editora
IOS Press