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Advisor(s)
Abstract(s)
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.
Description
Keywords
Clustering methods Finite mixture models Interval-valued variable Intrinsic variability Symbolic data
Pedagogical Context
Citation
BRITO, Paula; DUARTE SILVA, A. P.; DIAS, José G. - Probabilistic Clustering of Interval Data. Intelligent Data Analysis. 1571-4128. Vol. 19, n.º 2 (2015), p. 293-313
Publisher
IOS Press