Skip to main content
SpringerLink
Log in

Data Science, Learning by Latent Structures, and Knowledge Discovery

  • Conference proceedings
  • © 2015

Overview

Editors:
  1. Berthold Lausen

    1. University of Essex, Colchester, United Kingdom

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  2. Sabine Krolak-Schwerdt

    1. University of Luxembourg, Walferdange, Luxembourg

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  3. Matthias Böhmer

    1. University of Luxembourg, Walferdange, Luxembourg

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • Covers theory, methods and applications of data analysis
  • Inspires for further research in the fields of Data Analysis, Learning by Latent Structures and Knowledge Discovery
  • Combines the intensive work of excellent researchers from different disciplines united under the roof of data analysis

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (48 papers)

  1. Front Matter

    Pages i-xxii
    Download chapter PDF

  2. Invited Papers

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Modernising Official Statistics: A Complex Challenge

      • August Götzfried
      Pages 3-11

    3. A New Supervised Classification of Credit Approval Data via the Hybridized RBF Neural Network Model Using Information Complexity

      • Oguz Akbilgic, Hamparsum Bozdogan
      Pages 13-27

    4. Finding the Number of Disparate Clusters with Background Contamination

      • Anthony C. Atkinson, Andrea Cerioli, Gianluca Morelli, Marco Riani
      Pages 29-42

    5. Clustering of Solar Irradiance

      • 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
      Download chapter PDF

    2. Factor Analysis of Local Formalism

      • François Bavaud, Christelle Cocco
      Pages 57-67

    3. Recent Progress in Complex Network Analysis: Models of Random Intersection Graphs

      • Mindaugas Bloznelis, Erhard Godehardt, Jerzy Jaworski, Valentas Kurauskas, Katarzyna Rybarczyk
      Pages 69-78

    4. Recent Progress in Complex Network Analysis: Properties of Random Intersection Graphs

      • Mindaugas Bloznelis, Erhard Godehardt, Jerzy Jaworski, Valentas Kurauskas, Katarzyna Rybarczyk
      Pages 79-88

    5. Similarity Measures of Concept Lattices

      • Florent Domenach
      Pages 89-99

    6. Flow-Based Dissimilarities: Shortest Path, Commute Time, Max-Flow and Free Energy

      • Guillaume Guex, François Bavaud
      Pages 101-111

    7. Resampling Techniques in Cluster Analysis: Is Subsampling Better Than Bootstrapping?

      • Hans-Joachim Mucha, Hans-Georg Bartel
      Pages 113-122

    8. On-Line Clustering of Functional Boxplots for Monitoring Multiple Streaming Time Series

      • Elvira Romano, Antonio Balzanella
      Pages 123-132

    9. Smooth Tests of Fit for Gaussian Mixtures

      • Thomas Suesse, John Rayner, Olivier Thas
      Pages 133-142

  4. Machine Learning and Knowledge Discovery

    1. Front Matter

      Pages 143-143
      Download chapter PDF

    2. P2P RVM for Distributed Classification

      • Muhammad Umer Khan, Alexandros Nanopoulos, Lars Schmidt-Thieme
      Pages 145-155

    3. Selecting a Multi-Label Classification Method for an Interactive System

      • Noureddine-Yassine NAIR-BENREKIA, Pascale Kuntz, Frank Meyer
      Pages 157-167

    4. Visual Analysis of Topics in Twitter Based on Co-evolution of Terms

      • Lambert Pépin, Julien Blanchard, Fabrice Guillet, Pascale Kuntz, Philippe Suignard
      Pages 169-178

    5. Incremental Weighted Naive Bays Classifiers for Data Stream

      • Christophe Salperwyck, Vincent Lemaire, Carine Hue
      Pages 179-190
Back to top

Keywords

About this book

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.

Reviews

“The book under review is strongly recommended to interested theoreticians with strong background in measure and probability theory. Each chapter of the book is accompanied by supportive literature.” (Iris Burkholder, (Saarbrücken), British Journal of Hospital Medicine, Vol. 79 (5), May, 2018)

Editors and Affiliations

  • University of Essex, Colchester, United Kingdom

    Berthold Lausen

  • University of Luxembourg, Walferdange, Luxembourg

    Sabine Krolak-Schwerdt, Matthias Böhmer

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation