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Clustering Signals Using Wavelets

  • Michel Misiti
  • Yves Misiti
  • Georges Oppenheim
  • Jean-Michel Poggi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4507)

Abstract

A wavelet-based procedure for clustering signals is proposed. It combines an individual signal preprocessing by wavelet denoising, a dimensionality reduction step by wavelet compression and a classical clustering strategy applied to a suitably chosen set of wavelet coefficients. The ability of wavelets to cope with signals of arbitrary or time-dependent regularity as well as to concentrate signal energy in few large coefficients, offers a useful tool to carry out both significant noise reduction and efficient compression. A simulated example and an electrical dataset are considered to illustrate the value of introducing wavelets for clustering such complex data.

Keywords

Clustering Compression Denoising Signals Wavelets 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Michel Misiti
    • 1
    • 2
  • Yves Misiti
    • 1
  • Georges Oppenheim
    • 1
    • 3
  • Jean-Michel Poggi
    • 1
    • 4
  1. 1.Laboratoire de Mathématique, Université Paris-Sud, Bât. 425, 91405 OrsayFrance
  2. 2.Ecole Centrale de LyonFrance
  3. 3.Université de Marne-la-ValléeFrance
  4. 4.Université Paris 5 DescartesFrance

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