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An Improved Method for Estimating the Modes of the Probability Density Function and the Number of Classes for PDF-based Clustering

  • Michel Herbin
  • Noel Bonnet
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

This paper proposes an improvement of the estimation of the modes of the Probability Density Function (PDF) in clustering procedures. The k nearest neighbours are excluded when the PDF is estimated trying to avoid parasitic modes of the PDF estimation. The number of detected modes is analyzed using the bootstrap technique. The number of clusters is equal to the most frequent number of modes obtained when resampling the data (if the frequency is greater than 50%). The method to estimate the number of clusters is illustrated by an example in image processing.

Keywords

Probability Density Func Probability Density Function Gaussian Mixture Model Smoothing Parameter Cluster Procedure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Michel Herbin
    • 1
  • Noel Bonnet
    • 1
    • 2
  1. 1.IUT Leonard de VinciLERI, University of ReimsReims cedexFrance
  2. 2.INSERM Unit 514 (UMRS, IFR 53)Reims cedexFrance

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