Classification, Automation, and New Media

1. Front Matter

   Pages i-xi

   PDF

2. Data Analysis, Statistics, and Classification

   1. Front Matter

      Pages 1-1

      PDF

   2. Clustering and Models

      • Christophe Ambroise, Gerard Govaert

      Pages 3-16

   3. Some New and Some Old Results for the Polytomous Rasch Model

      • Erling B. Andersen

      Pages 17-27

   4. Some Basic Results on the Extension of Quasi-likelihood Based Measurement Error Correction to Multivariate and Flexible Structural Models

      • T. Augustin

      Pages 29-36

   5. On the Optimal Number of Clusters in Histogram Clustering

      • J. M. Buhmann, M. Held

      Pages 37-45

   6. The Dual Dynamic Factor Analysis Models

      • R. Coppi, P. D’Urso

      Pages 47-58

   7. Joint Non-symmetrical Correspondence Analysis with Ordered Categories

      • L. D’Ambra, R. Lombardo, P. Amenta

      Pages 59-66

   8. Regression Analysis of Extremely Multicollinear Data

      • Norman Fickel

      Pages 67-74

   9. Modern Data Analysis: A Clash of Paradigms

      • David J. Hand

      Pages 75-85

   10. Two-mode Clustering with Genetic Algorithms

       • J. Hansohm

       Pages 87-93

   11. Exact Tests for the Comparison of Binary Data Structures in Time

       • J. Krauth

       Pages 95-106

   12. Connected Maximum Split Clustering of Ladder Graphs

       • I. Lari

       Pages 107-114

   13. A Generalization of Two-mode Three-way Asymmetric Multidimensional Scaling

       • A. Okada, T. Imaizumi

       Pages 115-122

   14. Central Limit Theorem for Probabilities of Correct Classification

       • Wolf-Dieter Richter

       Pages 123-130

   15. A New Model for the Analysis of Multitrait-multimethod Data

       • R. Rocci, G. Bove

       Pages 131-138

   16. Discrete and Continuous Models for Two-way Data

       • Maurizio Vichi

       Pages 139-147

3. Pattern Recognition and Automation

   1. Front Matter

      Pages 149-149

      PDF

   2. Towards Feature Fusion — A Classifier on the Basis of Automatically Generated Significant Contour Sections

      • D. Pechtel, K.-D. Kuhnert

      Pages 151-158

   3. Convex Discriminant Analysis Tools for Non Convex Pattern Recognition

      • M. Rémon

      Pages 159-166

Given the huge amount of information in the internet and in practically every domain of knowledge that we are facing today, knowledge discovery calls for automation. The book deals with methods from classification and data analysis that respond effectively to this rapidly growing challenge. The interested reader will find new methodological insights as well as applications in economics, management science, finance, and marketing, and in pattern recognition, biology, health, and archaeology.

• Analysis
• Cluster analysis
• Estimator
• Factor analysis
• Regression analysis
• Resampling
• Sage
• Support Vector Machine
• algorithms
• best fit
• clustering
• data analysis
• information
• linear optimization
• multidimensional scaling

• Institute for Decision Theory and Operations Research, University of Karlsruhe, Karlsruhe, Germany

  Wolfgang Gaul

• Department of Mathematics and Informatics, University of Passau, Passau, Germany

  Gunter Ritter

Policies and ethics