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Advanced Studies in Classification and Data Science

  • Conference proceedings
  • © 2020

Overview

Editors:
  1. Tadashi Imaizumi

    1. School of Management and Information Sciences, Tama University, Tokyo, Japan

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  2. Akinori Okada

    1. Rikkyo University, Tokyo, Japan

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  3. Sadaaki Miyamoto

    1. University of Tsukuba, Tsukuba, Japan

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  4. Fumitake Sakaori

    1. Department of Mathematics, Chuo University, Tokyo, Japan

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  5. Yoshiro Yamamoto

    1. Department of Mathematics, Tokai University, Hiratsuka-shi, Japan

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  6. Maurizio Vichi

    1. Department of Statistical Sciences, Sapienza University of Rome, Roma, Italy

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  • Focuses on analysis of device data in data science
  • Applies methods and techniques of data science to marketing science, economics, and related fields
  • Builds an interface between statistical methods and applications in data science

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Table of contents (40 papers)

  1. Front Matter

    Pages i-xi
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  2. Classification and Cluster Analysis

    1. Front Matter

      Pages 1-1
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    2. Multilevel Model-Based Clustering: A New Proposal of Maximum-A-Posteriori Assignment

      • Silvia Bacci, Francesco Bartolucci, Fulvia Pennoni
      Pages 3-17

    3. Multi-Criteria Classifications in Regional Development Modelling

      • Beata Bal-DomaÅ„ska
      Pages 19-29

    4. Non-parametric Latent Modeling and Network Clustering

      • François Bavaud
      Pages 31-44

    5. Efficient, Geometrically Adaptive Techniques for Multiscale Gaussian-Kernel SVM Classification

      • Guangliang Chen
      Pages 45-56

    6. Random Forests Followed by Computed ABC Analysis as a Feature Selection Method for Machine Learning in Biomedical Data

      • Jörn Lötsch, Alfred Ultsch
      Pages 57-69

    7. Non-hierarchical Clustering for Large Data Without Recalculating Cluster Center

      • Atsuho Nakayama, Deguchi Shinji
      Pages 71-78

    8. Supervised Nested Algorithm for Classification Based on K-Means

      • Luciano Nieddu, Donatella Vicari
      Pages 79-88

    9. Using Classification of Regions Based on the Complexity of the Global Progress Indices for Supporting Development in Competitiveness

      • Éva Sándor-Kriszt, Anita Csesznák
      Pages 89-101

    10. Estimation Methods Based on Weighting Clusters

      • Roland Szilágyi, Beatrix Varga, Renáta Géczi-Papp
      Pages 103-113

  3. Multidimensional Analysis and Visualization

    1. Front Matter

      Pages 115-115
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    2. Five Strategies for Accommodating Overdispersion in Simple Correspondence Analysis

      • Eric J. Beh, Rosaria Lombardo
      Pages 117-129

    3. From Joint Graphical Display to Bi-Modal Clustering: [2] Dual Space Versus Total Space

      • José G. Clavel, Shizuhiko Nishisato
      Pages 131-143

    4. Linear Time Visualization and Search in Big Data Using Pixellated Factor Space Mapping

      • Fionn Murtagh
      Pages 145-155

    5. From Joint Graphical Display to Bi-Modal Clustering: [1] A Giant Leap in Quantification Theory

      • Shizuhiko Nishisato
      Pages 157-168

    6. External Logistic Biplots for Mixed Types of Data

      • José L. Vicente-Villardón, Julio C. Hernández-Sánchez
      Pages 169-183

  4. Statistical Methods

    1. Front Matter

      Pages 185-185
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    2. Functional Clustering Approach for Analysis of Concentration

      • Yifan Chen, Yuriko Komiya, Hiroyuki Minami, Masahiro Mizuta
      Pages 187-198

    3. Generalized Additive Models for the Detection of Copy Number Variations (CNVs) Using Multi-gene Panel Sequencing Data

      • Corinna Ernst, Rita K. Schmutzler, Eric Hahnen
      Pages 199-213
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Keywords

About this book

This edited volume focuses on the latest developments in classification and data science and covers a wide range of topics in the context of data analysis and related areas, e.g. the analysis of complex data, analysis of qualitative data, methods for high-dimensional data, dimensionality reduction, data visualization, multivariate statistical methods, and various applications to real data in the social sciences, medical sciences, and other disciplines.

In addition to sharing theoretical and methodological findings, the book shows how to apply the proposed methods to a variety of problems — e.g. in consumer behavior, decision-making, marketing data and social network structures. Both methodological aspects and applications to a wide range of areas such as economics, behavioral science, marketing science, management science and the social sciences are covered.

The book is chiefly intended for researchers and practitioners who are interested in the latest developments and practical applications in these fields, as well as applied statisticians and data analysts. Its combination of methodological advances with a wide range of real-world applications gathered from several fields makes it of unique value in helping readers solve their research problems.

Editors and Affiliations

  • School of Management and Information Sciences, Tama University, Tokyo, Japan

    Tadashi Imaizumi

  • Rikkyo University, Tokyo, Japan

    Akinori Okada

  • University of Tsukuba, Tsukuba, Japan

    Sadaaki Miyamoto

  • Department of Mathematics, Chuo University, Tokyo, Japan

    Fumitake Sakaori

  • Department of Mathematics, Tokai University, Hiratsuka-shi, Japan

    Yoshiro Yamamoto

  • Department of Statistical Sciences, Sapienza University of Rome, Roma, Italy

    Maurizio Vichi

About the editors

Tadashi Imaizumi, School of Management & Information Sciences, Tama University, Tokyo, Japan

Akinori Okada, Rikkyo University, Toshima-ku, Japan

Sadaaki Miyamoto, University of Tsukuba, Ibaraki, Japan

Fumitake Sakaori, Department of Mathematics, Chuo University, Tokyo, Japan

Yoshiro Yamamoto, Department of Mathematics, Tokai University, Kanagawa, Japan

Maurizio Vichi, Department of Statistical Sciences, Sapienza University of Rome, Roma RM, Italy

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