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.
|Title of host publication||Studies in Theoretical and Applied Statistics. SIS 2016.|
|Number of pages||10|
|Publication status||Published - 2018|
|Name||SPRINGER PROCEEDINGS IN MATHEMATICS & STATISTICS|