Clustering Spatial Functional Data: A Method Based on a Nonparametric Variogram Estimation

In this paper we propose an extended version of a model-based strategy for clustering spatial functional data. The strategy, we refer, aims simultaneously to classify spatially dependent curves and to obtain a spatial functional model prototype for each cluster. The fit of these models implies to estimate a variogram function, the trace variogram function. Our proposal is to introduce an alternative estimator for the trace-variogram function: a kernel variogram estimator. This works better to adapt spatial varying features of the functional data pattern. Experimental comparisons show this approach has some advantages over the previous one.