Abstract
We propose a new algorithm for clustering spatially dependent functional data that accounts for spatial dependence by repeatedly clustering functional local representatives of a random system of neighborhoods. The algorithm output is the frequency distribution of cluster assignment for each site of a given map. We illustrate different implementations of the algorithm by analyzing synthetic and real data.
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© 2011 Springer-Verlag Berlin Heidelberg
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Secchi, P., Vantini, S., Vitelli, V. (2011). Spatial Clustering of Functional Data. In: Ferraty, F. (eds) Recent Advances in Functional Data Analysis and Related Topics. Contributions to Statistics. Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-2736-1_44
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DOI: https://doi.org/10.1007/978-3-7908-2736-1_44
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Publisher Name: Physica-Verlag HD
Print ISBN: 978-3-7908-2735-4
Online ISBN: 978-3-7908-2736-1
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