Space-Time FPCA Clustering of Multidimensional Curves.

Marcello Chiodi, Giada Adelfio, Francesca Di Salvo, Francesca Di Salvo, Marcello Chiodi, Giada Adelfio

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

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.
Original languageEnglish
Title of host publicationStudies in Theoretical and Applied Statistics. SIS 2016.
Number of pages10
Publication statusPublished - 2018

Publication series

NameSPRINGER PROCEEDINGS IN MATHEMATICS & STATISTICS

All Science Journal Classification (ASJC) codes

  • General Mathematics

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