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Clustering Based on Wavelet Transform: Applications to Point Pattern Clustering and to High-Dimensional Data Analysis

  • F. Murtagh
  • J. L. Starck
  • M. Berry
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

We describe an effective approach to object or feature detection in point patterns via noise modeling. This is based on use of a redundant or non-pyramidal wavelet transform. Noise modeling is based on a Poisson process. We illustrate this new method with a range of examples. We use the close relationship between image (pixelated) and point representations to achieve the result of a clustering method with constant-time computational cost. We then proceed to generalize this method for high-dimensional data. Using a dataset of very well-known structure as a test case, we show proof of concept for this approach to analysis of high-dimensional boolean hyperlink datasets.

Keywords

Cluster Analysis Wavelet Transform Multiresolution Analysis 

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Copyright information

© Springer-Verlag Berlin · Heidelberg 1998

Authors and Affiliations

  • F. Murtagh
    • 1
  • J. L. Starck
    • 2
  • M. Berry
    • 3
  1. 1.Faculty of InformaticsUniversity of UlsterNorthern Ireland
  2. 2.CEA/DSM/DAPNIAGif-sur-Yvette cedexFrance
  3. 3.Department of Computer ScienceUniversity of TennesseeKnoxvilleUSA

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