Data Science, Learning by Latent Structures, and Knowledge Discovery

  • Berthold Lausen
  • Sabine Krolak-Schwerdt
  • Matthias Böhmer

Table of contents

  1. Front Matter
    Pages i-xxii
  2. Invited Papers

    1. Front Matter
      Pages 1-1
    2. Anthony C. Atkinson, Andrea Cerioli, Gianluca Morelli, Marco Riani
      Pages 29-42
    3. Miloud Bessafi, Franciscos de A.T. de Carvalho, Philippe Charton, Mathieu Delsaut, Thierry Despeyroux, Patrick Jeanty et al.
      Pages 43-53
  3. Data Science and Clustering

    1. Front Matter
      Pages 55-55
    2. François Bavaud, Christelle Cocco
      Pages 57-67
    3. Mindaugas Bloznelis, Erhard Godehardt, Jerzy Jaworski, Valentas Kurauskas, Katarzyna Rybarczyk
      Pages 69-78
    4. Mindaugas Bloznelis, Erhard Godehardt, Jerzy Jaworski, Valentas Kurauskas, Katarzyna Rybarczyk
      Pages 79-88
    5. Florent Domenach
      Pages 89-99
    6. Thomas Suesse, John Rayner, Olivier Thas
      Pages 133-142
  4. Machine Learning and Knowledge Discovery

    1. Front Matter
      Pages 143-143
    2. Muhammad Umer Khan, Alexandros Nanopoulos, Lars Schmidt-Thieme
      Pages 145-155
    3. Noureddine-Yassine NAIR-BENREKIA, Pascale Kuntz, Frank Meyer
      Pages 157-167
    4. Lambert Pépin, Julien Blanchard, Fabrice Guillet, Pascale Kuntz, Philippe Suignard
      Pages 169-178
    5. Christophe Salperwyck, Vincent Lemaire, Carine Hue
      Pages 179-190

About these proceedings


This volume comprises papers dedicated to data science and the extraction of knowledge from many types of data: structural, quantitative, or statistical approaches for the analysis of data; advances in classification, clustering, and pattern recognition methods; strategies for modeling complex data and mining large data sets; applications of advanced methods in specific domains of practice. The contributions offer interesting applications to various disciplines such as psychology, biology, medical and health sciences; economics, marketing, banking, and finance; engineering; geography and geology;  archeology, sociology, educational sciences, linguistics, and musicology; library science. The book contains the selected and peer-reviewed papers presented during the European Conference on Data Analysis (ECDA 2013) which was jointly held by the German Classification Society (GfKl) and the French-speaking Classification Society (SFC) in July 2013 at the University of Luxembourg.


Classification Data Analysis Data Science Data Stream Knowledge Organization Latent Structures

Editors and affiliations

  • Berthold Lausen
    • 1
  • Sabine Krolak-Schwerdt
    • 2
  • Matthias Böhmer
    • 3
  1. 1.University of EssexColchesterUnited Kingdom
  2. 2.University of LuxembourgWalferdangeLuxembourg
  3. 3.University of LuxembourgWalferdangeLuxembourg

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2015
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-662-44982-0
  • Online ISBN 978-3-662-44983-7
  • Series Print ISSN 1431-8814
  • Series Online ISSN 2198-3321
  • About this book