In this paper we propose two clustering strategies for spatially referenced functional data. Both are model based approaches since a functional linear model is supposed to generate the elements of each cluster. A first proposal considers the problem of clustering curves that are realization of a random stochastic process. In this case functional sub-processes are discovered and their generative models are estimated in optimal unsampled locations of the space. A second one achieves simultaneously an optimal partition of the data and functional linear models which incorporate the spatial interaction among different functional variable in each cluster.

Spatio-Functional data Analysis: clustering methods for models discovering

ROMANO, Elvira
2009

Abstract

In this paper we propose two clustering strategies for spatially referenced functional data. Both are model based approaches since a functional linear model is supposed to generate the elements of each cluster. A first proposal considers the problem of clustering curves that are realization of a random stochastic process. In this case functional sub-processes are discovered and their generative models are estimated in optimal unsampled locations of the space. A second one achieves simultaneously an optimal partition of the data and functional linear models which incorporate the spatial interaction among different functional variable in each cluster.
978-88-6129-425-7
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11591/205654
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