Skip to main content
SpringerLink
Log in

Classification, Clustering, and Data Mining Applications

Proceedings of the Meeting of the International Federation of Classification Societies (IFCS), Illinois Institute of Technology, Chicago, 15–18 July 2004

  • Conference proceedings
  • © 2004

Overview

Editors:
  1. David Banks

    1. Leanna House Institute of Statistics and Decision Sciences, Duke University, Durham, USA

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  2. Frederick R. McMorris

    1. Department of Mathematics, Illinois Institute of Technology, Chicago, USA

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  3. Phipps Arabie

    1. Faculty of Management, Rutgers University, Newark, USA

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  4. Wolfgang Gaul

    1. Institute of Decision Theory, University of Karlsruhe, Karlsruhe, Germany

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (61 papers)

  1. Front Matter

    Pages I-XIV
    Download chapter PDF

  2. New Methods in Cluster Analysis

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Thinking Ultrametrically

      • Fionn Murtagh
      Pages 3-14

    3. Clustering by Vertex Density in a Graph

      • Alain Guénoche
      Pages 15-23

    4. Clustering by Ant Colony Optimization

      • Javier Trejos, Alex Murillo, Eduardo Piza
      Pages 25-32

    5. A Dynamic Cluster Algorithm Based on L r Distances for Quantitative Data

      • Francisco de A. T. de Carvalho, Yves Lechevallier, Renata M. C. R. de Souza
      Pages 33-41

    6. The Last Step of a New Divisive Monothetic Clustering Method: the Gluing-Back Criterion

      • Jean-Yves Pirçon, Jean-Paul Rasson
      Pages 43-51

    7. Standardizing Variables in K-means Clustering

      • Douglas Steinley
      Pages 53-60

    8. A Self-Organizing Map for Dissimilarity Data

      • Aïcha El Golli, Brieuc Conan-Guez, Fabrice Rossi
      Pages 61-68

    9. Another Version of the Block EM Algorithm

      • Mohamed Nadif, Gérard Govaert
      Pages 69-76

    10. Controlling the Level of Separation of Components in Monte Carlo Studies of Latent Class Models

      • José G. Dias
      Pages 77-84

    11. Fixing Parameters in the Constrained Hierarchical Classification Method: Application to Digital Image Segmentation

      • Kaddour Bachar, Israël-César Lerman
      Pages 85-94

    12. New Approaches for Sum-of-Diameters Clustering

      • Sarnath Ramnath, Mynul H. Khan, Zubair Shams
      Pages 95-103

    13. Spatial Pyramidal Clustering Based on a Tessellation

      • Edwin Diday
      Pages 105-120

  3. Modern Nonparametrics

    1. Front Matter

      Pages 121-121
      Download chapter PDF

    2. Relative Projection Pursuit and its Application

      • Masahiro Mizuta, Shintaro Hiro
      Pages 123-139

    3. Priors for Neural Networks

      • Herbert K. H. Lee
      Pages 141-150

    4. Combining Models in Discrete Discriminant Analysis Through a Committee of Methods

      • Ana Sousa Ferreira
      Pages 151-156

    5. Phoneme Discrimination with Functional Multi-Layer Perceptrons

      • Brieuc Conan-Guez, Fabrice Rossi
      Pages 157-165

    6. PLS Approach for Clusterwise Linear Regression on Functional Data

      • Cristian Preda, Gilbert Saporta
      Pages 167-176
Back to top

Keywords

About this book

Modern data analysis stands at the interface of statistics, computer science, and discrete mathematics. This volume describes new methods in this area, with special emphasis on classification and cluster analysis. Those methods are applied to problems in information retrieval, phylogeny, medical diagnosis, microarrays, and other active research areas.

Editors and Affiliations

  • Leanna House Institute of Statistics and Decision Sciences, Duke University, Durham, USA

    David Banks

  • Department of Mathematics, Illinois Institute of Technology, Chicago, USA

    Frederick R. McMorris

  • Faculty of Management, Rutgers University, Newark, USA

    Phipps Arabie

  • Institute of Decision Theory, University of Karlsruhe, Karlsruhe, Germany

    Wolfgang Gaul

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation