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Clustering Functional Data Using Wavelets Anestis Antoniadis1
 

Summary: Clustering Functional Data Using Wavelets
Anestis Antoniadis1
, Xavier Brossat2
, Jairo Cugliari2,3
, and Jean-Michel
Poggi3,4
1
Universit´e Joseph Fourier, Laboratoire LJK, Tour IRMA, BP53, 38041
Grenoble Cedex 9, France, anestis.antoniadis@imag.fr
2
EDF R&D, 1 avenue du G´en´eral de Gaulle, 92141 Clamart Cedex, France,
xavier.brossat@edf.fr
3
Universit´e Paris-Sud, Math´ematique B^at. 425, 91405 Orsay, France
jairo.cugliari@math.u-psud.fr, jean-michel.poggi@math.u-psud.fr
4
Universit´e Paris 5 Descartes, France
Abstract. This paper presents a method for effectively detecting patterns and
clusters in high dimensional time-dependent functional data. It is based on wavelet-
based similarity measures since wavelets are ideal for identifying highly discriminant

  

Source: Antoniadis, Anestis - Laboratoire Jean Kuntzmann, Université Joseph Fourier

 

Collections: Mathematics