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Author(s): Brito, P.
Silva, A. P. D.
Dias, J. G.
Date: 2015
Title: Probabilistic clustering of interval data
Volume: 19
Number: 2
Pages: 293 - 313
ISSN: 1088-467X
DOI (Digital Object Identifier): 10.3233/IDA-150718
Keywords: Clustering methods
Finite mixture models
Interval-valued variable
Intrinsic variability
Symbolic data
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.
Peerreviewed: yes
Access type: Open Access
Appears in Collections:BRU-RI - Artigos em revistas científicas internacionais com arbitragem científica

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