In this paper we focus on finding clusters of multidimensional curves with spatio-temporal structure, applying a variant of a k-means algorithm based on the principal component rotation of data. The main advantage of this approach is to combine the clustering functional analysis of the multidimensional data, with smoothing methods based on generalized additive models, that cope with both the spatial and the temporal variability, and with functional principal components that takes into account the dependency between the curves.

Adelfio, G., Di Salvo, F., Chiodi, M. (2016). Space-time FPCA Algorithm for clustering of multidimensional curves.. In Proceeding of the 48th Scientific Meeting of the Italian Statistical Society, (pp.1-12). Editors: Monica Pratesi and Cira Pena..

Space-time FPCA Algorithm for clustering of multidimensional curves.

ADELFIO, Giada;DI SALVO, Francesca;CHIODI, Marcello
2016-01-01

Abstract

In this paper we focus on finding clusters of multidimensional curves with spatio-temporal structure, applying a variant of a k-means algorithm based on the principal component rotation of data. The main advantage of this approach is to combine the clustering functional analysis of the multidimensional data, with smoothing methods based on generalized additive models, that cope with both the spatial and the temporal variability, and with functional principal components that takes into account the dependency between the curves.
giu-2016
48th Scientific Meeting of the Italian Statistical Society
Salerno
June 8th – June 10th, 2016.
48
12
Adelfio, G., Di Salvo, F., Chiodi, M. (2016). Space-time FPCA Algorithm for clustering of multidimensional curves.. In Proceeding of the 48th Scientific Meeting of the Italian Statistical Society, (pp.1-12). Editors: Monica Pratesi and Cira Pena..
Proceedings (atti dei congressi)
Adelfio,G; Di Salvo, F; Chiodi, M
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/180210
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