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http://hdl.handle.net/10071/8899
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Title: Probabilistic clustering of interval data
Authors: Brito, P.
Silva, A. P. D.
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.
Peer reviewed: yes
URI: http://hdl.handle.net/10071/8899
DOI: 10.3233/IDA-150718
ISSN: 1088-467X
Ciência-IUL: https://ciencia.iscte-iul.pt/id/ci-pub-14299
Accession number: WOS:000353062400006
Appears in Collections:BRU-RI - Artigos em revistas científicas internacionais com arbitragem científica

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