Robust fuzzyC-Medoids clustering models based on B-splines with spatial penalty term have been proposed to cluster Italian regions according to the daily time-series of the cumulative COVID-19 cases over population (per 10000 inhabitants) and of the cumulative COVID-19 deaths over population (per 10000 inhabitants), spanning from 2020-02-24 to 2021-02-08. Both spatial and time components have been efficiently embedded in the model. Furthermore the use of B-splines coefficients allows to reduce consistently the computational burdern.
Spatial-temporal clustering based on B-splines: robust models with applications to COVID-19 pandemic / D'Urso, Pierpaolo; De Giovanni, Livia; Vitale, Vincenzina. - 128:(2021), pp. 83-86. (Intervento presentato al convegno 13th Scientific Meeting of the Classification and Data Analysis Group tenutosi a Firenze) [10.36253/978-88-5518-340-6].
Spatial-temporal clustering based on B-splines: robust models with applications to COVID-19 pandemic
Pierpaolo D’Urso;Vincenzina Vitale
2021
Abstract
Robust fuzzyC-Medoids clustering models based on B-splines with spatial penalty term have been proposed to cluster Italian regions according to the daily time-series of the cumulative COVID-19 cases over population (per 10000 inhabitants) and of the cumulative COVID-19 deaths over population (per 10000 inhabitants), spanning from 2020-02-24 to 2021-02-08. Both spatial and time components have been efficiently embedded in the model. Furthermore the use of B-splines coefficients allows to reduce consistently the computational burdern.| File | Dimensione | Formato | |
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