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Title: Probabilistic clustering of interval data
Authors: Brito, M. P.
Duarte Silva, P.
Dias, J. G.
Keywords: Clustering methods
Finite mixture models
Interval-valued variable
Intrinsic variability
Symbolic data
Issue Date: 2015
Publisher: IOS Press
Abstract: 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.
ISSN: 1088-467X
Appears in Collections:BRU-RI - Artigo em revista científica internacional com arbitragem científica

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