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Data Science, Classification, and Related Methods

Proceedings of the Fifth Conference of the International Federation of Classification Societies (IFCS-96), Kobe, Japan, March 27–30, 1996

  • Chikio Hayashi
  • Keiji Yajima
  • Hans-Hermann Bock
  • Noboru Ohsumi
  • Yutaka Tanaka
  • Yasumasa Baba

Table of contents

  1. Related Approaches for Classification

    1. Symbolic and Conceptual Data Analysis

      1. R. Gras, H. Briand, P. Peter, J. Philippe
        Pages 412-419
  2. Correspondence Analysis, Quantification Methods, and Multidimensional Scaling

    1. Front Matter
      Pages 421-421
    2. Correspondence Analysis and Its Application

      1. Shizuhiko Nishisato
        Pages 441-451
      2. Matevž Bren, Vladimir Batagelj
        Pages 460-467
    3. Classification of Textual Data

    4. Multidimensional Scaling

  3. Multivariate and Multidimensional Data Analysis

    1. Front Matter
      Pages 525-525
    2. Multidimensional Data Analysis

    3. Multiway Data Analysis

    4. Non-Linear Modeling and Visual Treatment

  4. Case Studies of Data Science

    1. Front Matter
      Pages 645-645
    2. Social Science and Behavioral Science

    3. Management Science and Marketing Science

    4. Environmental, Ecological, Biological, and Medical Sciences

  5. Back Matter
    Pages 779-780

About these proceedings

Introduction

This volume, Data Science, Classification, and Related Methods, contains a selection of papers presented at the Fifth Conference of the International Federation of Oassification Societies (IFCS-96), which was held in Kobe, Japan, from March 27 to 30,1996. The volume covers a wide range of topics and perspectives in the growing field of data science, including theoretical and methodological advances in domains relating to data gathering, classification and clustering, exploratory and multivariate data analysis, and knowledge discovery and seeking. It gives a broad view of the state of the art and is intended for those in the scientific community who either develop new data analysis methods or gather data and use search tools for analyzing and interpreting large and complex data sets. Presenting a wide field of applications, this book is of interest not only to data analysts, mathematicians, and statisticians but also to scientists from many areas and disciplines concerned with complex data: medicine, biology, space science, geoscience, environmental science, infonnation science, image and pattern analysis, economics, statistics, social sciences, psychology, cognitive science, behavioral science, marketing and survey research, data mining, and knowledge organization.

Keywords

calculus classification data analysis decision making multidimensional scaling

Editors and affiliations

  • Chikio Hayashi
    • 1
  • Keiji Yajima
    • 2
  • Hans-Hermann Bock
    • 3
  • Noboru Ohsumi
    • 1
  • Yutaka Tanaka
    • 4
  • Yasumasa Baba
    • 1
  1. 1.The Institute of Statistical MathematicsTokyo 106Japan
  2. 2.School of ManagementScience University of TokyoSaitama 346Japan
  3. 3.Institut für StatistikRheinisch-Westfälische Technische Hochschule (RWTH)AachenGermany
  4. 4.Faculty of Environmental Science & TechnologyOkayama UniversityOkayama 700Japan

Bibliographic information

  • DOI https://doi.org/10.1007/978-4-431-65950-1
  • Copyright Information Springer-Verlag Tokyo 1998
  • Publisher Name Springer, Tokyo
  • eBook Packages Springer Book Archive
  • Print ISBN 978-4-431-70208-5
  • Online ISBN 978-4-431-65950-1
  • Series Print ISSN 1431-8814
  • Buy this book on publisher's site