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Data Analysis, Machine Learning and Applications

Proceedings of the 31st Annual Conference of the Gesellschaft für Klassifikation e.V., Albert-Ludwigs-Universität Freiburg, March 7-9, 2007

  • Conference proceedings
  • © 2008

Overview

Editors:
  1. Christine Preisach

    1. Institute of Computer Science and Institute of Business Economics and Information Systems, University of Hildesheim, Hildesheim, Germany

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  2. Hans Burkhardt

    1. Lehrstuhl für Mustererkennung und Bildverarbeitung, Universität Freiburg, Freiburg i. Br, Germany

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  3. Lars Schmidt-Thieme

    1. Institute of Computer Science and Institute of Business Economics and Information Systems, Hildesheim, Germany

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  4. Reinhold Decker

    1. Fakultät für Wirtschaftswissenschaften, Lehrstuhl für Betriebswirtschaftslehre, insbes. Marketing, Bielefeld, Germany

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Table of contents (83 papers)

  1. Front Matter

    Pages I-XVI
    Download chapter PDF

  2. Classification

    1. Distance-Based Kernels for Real-Valued Data

      • Lluís Belanche, Jean Luis Vázquez, Miguel Vázquez
      Pages 3-10

    2. Fast Support Vector Machine Classification of Very Large Datasets

      • Janis Fehr, Karina Zapién Arreola, Hans Burkhardt
      Pages 11-18

    3. Fusion of Multiple Statistical Classifiers

      • Eugeniusz Gatnar
      Pages 19-27

    4. Calibrating Margin-Based Classifier Scores into Polychotomous Probabilities

      • Martin Gebel, Claus Weihs
      Pages 29-36

    5. Classification with Invariant Distance Substitution Kernels

      • Bernard Haasdonk, Hans Burkhardt
      Pages 37-44

    6. Applying the Kohonen Self-Organizing Map Networks to Select Variables

      • Kamila Migdał Najman, Krzysztof Najman
      Pages 45-54

    7. Computer Assisted Classification of Brain Tumors

      • Norbert Röhrl, José R. Iglesias-Rozas, Galia Weidl
      Pages 55-60

    8. Model Selection in Mixture Regression Analysis–A Monte Carlo Simulation Study

      • Marko Sarstedt, Manfred Schwaiger
      Pages 61-68

    9. Comparison of Local Classification Methods

      • Julia Schiffner, Claus Weihs
      Pages 69-76

    10. Incorporating Domain Specific Information into Gaia Source Classification

      • Kester W. Smith, Carola Tiede, Coryn A. L. Bailer-Jones
      Pages 77-83

    11. Identification of Noisy Variables for Nonmetric and Symbolic Data in Cluster Analysis

      • Marek Walesiak, Andrzej Dudek
      Pages 85-92

  3. Clustering

    1. Families of Dendrograms

      • Patrick Erik Bradley
      Pages 95-102

    2. Mixture Models in Forward Search Methods for Outlier Detection

      • Daniela G. Calò
      Pages 103-110

    3. On Multiple Imputation Through Finite Gaussian Mixture Models

      • Marco Di Zio, Ugo Guarnera
      Pages 111-118

    4. Mixture Model Based Group Inference in Fused Genotype and Phenotype Data

      • Benjamin Georgi, M. Anne Spence, Pamela Flodman, Alexander Schliep
      Pages 119-126

    5. The Noise Component in Model-based Cluster Analysis

      • Christian Hennig, Pietro Coretto
      Pages 127-138

    6. An Artificial Life Approach for Semi-supervised Learning

      • Lutz Herrmann, Alfred Ultsch
      Pages 139-146

    7. Hard and Soft Euclidean Consensus Partitions

      • Kurt Hornik, Walter Böhm
      Pages 147-154

    8. Rationale Models for Conceptual Modeling

      • Sina Lehrmann, Werner Esswein
      Pages 155-162
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Keywords

About this book

Data analysis and machine learning are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics. They cover general methods and techniques that can be applied to a vast set of applications such as web and text mining, marketing, medical science, bioinformatics and business intelligence. This volume contains the revised versions of selected papers in the field of data analysis, machine learning and applications presented during the 31st Annual Conference of the German Classification Society (Gesellschaft für Klassifikation - GfKl). The conference was held at the Albert-Ludwigs-University in Freiburg, Germany, in March 2007.

Reviews

From the reviews: “Data analysis and machine learning are potential research areas at the intersection of computer science (including artificial intelligence) and mathematical sciences (including both mathematics and statistics). This book contains a good collection of papers in these areas, describing their application in areas such as marketing and bioinformatics. … It is surely a must have book for any scientific library and indispensable for the scientific researcher.” (Soubhik Chakraborthy, ACM Computing Reviews, February, 2010)

Editors and Affiliations

  • Institute of Computer Science and Institute of Business Economics and Information Systems, University of Hildesheim, Hildesheim, Germany

    Christine Preisach

  • Lehrstuhl für Mustererkennung und Bildverarbeitung, Universität Freiburg, Freiburg i. Br, Germany

    Hans Burkhardt

  • Institute of Computer Science and Institute of Business Economics and Information Systems, Hildesheim, Germany

    Lars Schmidt-Thieme

  • Fakultät für Wirtschaftswissenschaften, Lehrstuhl für Betriebswirtschaftslehre, insbes. Marketing, Bielefeld, Germany

    Reinhold Decker

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