Section: New Results

Axis 3: Clustering spatial functional data

Participants : Sophie Dabo, Cristian Preda, Vincent Vandewalle.

We propose two approaches for clustering spatial functional data. The first one is the model-based clustering that uses the concept of density for functional random variables. The second one is the hierarchical clustering based on univariate statistics for functional data such as the functional mode or the functional mean. These two approaches take into account the spatial features of the data: two observations that are spatially close share a common distribution of the associated random variables. The two methodologies are illustrated by an application to air quality data. This work will appear in the “Geostatistical Functional Data Analysis: Theory and Methods”. Wiley, 2018. Editors : Jorge Mateu, Ramon Giraldo [39].