Unsupervised classification for multivariate functional data
Fecha
2018
Autores
Velasco Mendoza, Javier Antonio
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Editor
Resumen
In this paper, we propose a new method to classify functional data but in the
multivariate case. This new technique is based on some order and centrality
measures for the functional framework. Although our methodology works well
in the general case, in this work each record is composed of two functional
variables and the functional sample is composed of several records. We design a
statistical tool for segmenting that sample in groups with similar characteristics.
We test our methodology with real and simulated data and we highlight that
this new method introduced here, work better than those techniques already
introduced in the literature.