Please use this identifier to cite or link to this item:
http://hdl.handle.net/10071/8899
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 |
Files in This Item:
File | Description | Size | Format | |
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Dias 2014 Intelligent Data Analysis.pdf | Pós-print | 596,09 kB | Adobe PDF | View/Open |
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