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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Brito, P., Silva, A. P. D. & Dias, J. G. (2015). Probabilistic clustering of interval data. Intelligent Data Analysis. 19 (2), 293-313
Exportar Referência (IEEE) 
M. P. Brito et al.,  "Probabilistic clustering of interval data", in Intelligent Data Analysis, vol. 19, no. 2, pp. 293-313, 2015
Exportar BibTeX 
@article{brito2015_1732202840585,
	author = "Brito, P. and Silva, A. P. D. and Dias, J. G.",
	title = "Probabilistic clustering of interval data",
	journal = "Intelligent Data Analysis",
	year = "2015",
	volume = "19",
	number = "2",
	doi = "10.3233/IDA-150718",
	pages = "293-313",
	url = "http://iospress.metapress.com/content/1088-467X/"
}
Exportar RIS 
TY  - JOUR
TI  - Probabilistic clustering of interval data
T2  - Intelligent Data Analysis
VL  - 19
IS  - 2
AU  - Brito, P.
AU  - Silva, A. P. D.
AU  - Dias, J. G.
PY  - 2015
SP  - 293-313
SN  - 1088-467X
DO  - 10.3233/IDA-150718
UR  - http://iospress.metapress.com/content/1088-467X/
AB  - 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.
ER  -