Classification, Clustering, and Data Mining Applications

1. Front Matter

   Pages I-XIV

   PDF

2. New Methods in Cluster Analysis

   1. Front Matter

      Pages 1-1

      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

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   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

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.

• Cluster analysis
• Graph
• Projection pursuit
• Sim
• Vertex
• algorithms
• clustering
• complexity
• computer science
• data analysis
• data mining
• database
• expectation–maximization algorithm
• modeling
• optimization
• data structures

• 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

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