Exploratory analysis of functional data via clustering and optimal segmentation

Authors:
Georges Hébrail;Bernard Hugueney;Yves Lechevallier;Fabrice Rossi
Affiliations:
BILab, Télécom ParisTech, LTCI - UMR CNRS 5141 46, rue Barrault, 75013 Paris, France;LAMSADE, Université Paris Dauphine, Place du Maréchal de Lattre de Tassigny, 75016 Paris, France;Projet AxIS, INRIA, Domaine de Voluceau, Rocquencourt, B.P. 105 78153 Le Chesnay Cedex, France;BILab, Télécom ParisTech, LTCI - UMR CNRS 5141 46, rue Barrault, 75013 Paris, France
Venue:
Neurocomputing
Year:
2010

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Abstract

We propose in this paper an exploratory analysis algorithm for functional data. The method partitions a set of functions into K clusters and represents each cluster by a simple prototype (e.g., piecewise constant). The total number of segments in the prototypes, P, is chosen by the user and optimally distributed among the clusters via two dynamic programming algorithms. The practical relevance of the method is shown on two real world datasets.