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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

  • Conference proceedings
  • © 1998

Overview

Editors:
  1. Chikio Hayashi

    1. The Institute of Statistical Mathematics, Tokyo 106, Japan

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  2. Keiji Yajima

    1. School of Management, Science University of Tokyo, Saitama 346, Japan

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  3. Hans-Hermann Bock

    1. Institut für Statistik, Rheinisch-Westfälische Technische Hochschule (RWTH), Aachen, Germany

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  4. Noboru Ohsumi

    1. The Institute of Statistical Mathematics, Tokyo 106, Japan

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  5. Yutaka Tanaka

    1. Faculty of Environmental Science & Technology, Okayama University, Okayama 700, Japan

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  6. Yasumasa Baba

    1. The Institute of Statistical Mathematics, Tokyo 106, Japan

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  • Helps the reader to gather data and to use search tools for analyzing and interpreting large and complex data sets
  • Presents a wide field of applications of interest not only to data analysts, mathematicians, and statisticians but also to scientists concerned with hard data in disciplines as diverse as medicine, biology, and space science

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Table of contents (85 papers)

  1. Front Matter

    Pages I-XV
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  2. General Aspects of Data Science

    1. Front Matter

      Pages 1-1
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    2. Probabilistic Aspects in Classification

      • Hans H. Bock
      Pages 3-21

    3. Cluster Validation

      • A. D. Gordon
      Pages 22-39

    4. What is Data Science ? Fundamental Concepts and a Heuristic Example

      • Chikio Hayashi
      Pages 40-51

    5. Fitting Graphs and Trees with Multidimensional Scaling Methods

      • Willem J. Heiser
      Pages 52-62

    6. Classification and data analysis in finance

      • Krzysztof Jajuga
      Pages 63-70

    7. How to validate phylogenetic trees? A stepwise procedure

      • François-Joseph Lapointe
      Pages 71-88

    8. Some Trends in the Classification of Variables

      • F. Costa Nicolau, H. Bacelar-Nicolau
      Pages 89-98

    9. Convexity Methods in Classification

      • Jean-Paul Rasson
      Pages 99-106

  3. Methodologies in Classification

    1. Front Matter

      Pages 107-107
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    2. Evaluation and Assessment Procedures

      1. How Many Clusters? An Investigation of Five Procedures for Detecting Nested Cluster Structure
        • A. D. Gordon
        Pages 109-116
      2. Partitional Cluster Analysis with Genetic Algorithms: Searching for the Number of Clusters
        • J. A. Lozano, P. Larrañaga, M. Graña
        Pages 117-124
      3. Explanatory Variables in Classifications and the Detection of the Optimum Number of Clusters
        • János Podani
        Pages 125-132
      4. Random dendrograms for classifiability testing
        • Bernard Van Cutsem, Bernard Ycart
        Pages 133-144
      5. The <i>L</i> <sub> <i>p</i> </sub>-product of ultrametric spaces and the corresponding product of hierarchies
        • Bernard Fichet
        Pages 145-153
      6. Towards Comparison of Decomposable Systems
        • Mark Sh. Levin
        Pages 154-161
      7. Performance of Eight Dissimilarity Coefficients to Cluster a Compositional Data Set
        • Maria Cristina Martín
        Pages 162-169

    3. Topics in Clustering and Classification

      1. Consensus of Hierarchical Classifications
        • Bruno Simeone, Maurizio Vichi
        Pages 170-181
      2. On the Minimum Description Length (MDL) Principle for Hierarchical Classifications
        • Peter G. Bryant
        Pages 182-186
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Keywords

About this book

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.

Editors and Affiliations

  • The Institute of Statistical Mathematics, Tokyo 106, Japan

    Chikio Hayashi, Noboru Ohsumi, Yasumasa Baba

  • School of Management, Science University of Tokyo, Saitama 346, Japan

    Keiji Yajima

  • Institut für Statistik, Rheinisch-Westfälische Technische Hochschule (RWTH), Aachen, Germany

    Hans-Hermann Bock

  • Faculty of Environmental Science & Technology, Okayama University, Okayama 700, Japan

    Yutaka Tanaka

Bibliographic Information

  • Book Title: Data Science, Classification, and Related Methods

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

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

  • Series Title: Studies in Classification, Data Analysis, and Knowledge Organization

  • DOI: https://doi.org/10.1007/978-4-431-65950-1

  • Publisher: Springer Tokyo

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer Japan 1998

  • Softcover ISBN: 978-4-431-70208-5Published: 01 March 1998

  • eBook ISBN: 978-4-431-65950-1Published: 11 November 2013

  • Series ISSN: 1431-8814

  • Series E-ISSN: 2198-3321

  • Edition Number: 1

  • Number of Pages: XV, 780

  • Number of Illustrations: 98 b/w illustrations

  • Topics: Statistics, general, Economic Theory/Quantitative Economics/Mathematical Methods, Data Structures

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