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|Title:||Probabilistic clustering of interval data|
|Authors:||Brito, M. P.|
Duarte Silva, P.
Dias, J. G.
Finite mixture models
|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.|
|Appears in Collections:||BRU-RI - Artigo em revista científica internacional com arbitragem científica|
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|post_print_Dias 2014 Intelligent Data Analysis.pdf||535.56 kB||Adobe PDF||View/Open|
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