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A New Clustering Approach, Based on the Estimation of the Probability Density Function, for Gene Expression Data

  • Noël Bonnet
  • Michel Herbin
  • Jérôme Cutrona
  • Jean-Marie Zahm
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

Many techniques have already been suggested for handling and analyzing the large and high-dimensional data sets produced by newly developed gene expression experiments. These techniques include supervised classification and unsupervised agglomerative or hierarchical clustering techniques. Here, we present an alternative approach that does not make assumption on the shape, size and volumes of the clusters. The technique is based on the estimation of the probability density function (pdf). Once the pdf is estimated, with the Parzen technique (with the right amount of smoothing), the parameter space is partitioned according to methods inherited from image processing, namely the skeleton by influence zones and the watershed. We show some advantages of this suggested approach.

Keywords

Support Vector Machine Probability Density Function Dimensionality Reduction Factorial Axis Influence Zone 
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

  • Noël Bonnet
    • 1
    • 2
  • Michel Herbin
    • 2
  • Jérôme Cutrona
    • 1
    • 2
  • Jean-Marie Zahm
    • 1
  1. 1.Inserm Unit 514 (UMRS, IFR53)Reims cedexFrance
  2. 2.LERI, IUT Léonard de VinciUniversity of ReimsReims cedexFrance

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