Advances in Self-Organizing Maps and Learning Vector Quantization

Proceedings of the 10th International Workshop, WSOM 2014, Mittweida, Germany, July, 2-4, 2014

  • Thomas Villmann
  • Frank-Michael Schleif
  • Marika Kaden
  • Mandy Lange
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 295)

Table of contents

  1. Front Matter
    Pages 1-11
  2. SOM-Theory and Visualization Techniques

    1. Front Matter
      Pages 1-1
    2. Enrique Pelayo, David Buldain
      Pages 35-44
    3. Jérôme Mariette, Madalina Olteanu, Julien Boelaert, Nathalie Villa-Vialaneix
      Pages 45-54
    4. Diego H. Peluffo-Ordóñez, John A. Lee, Michel Verleysen
      Pages 65-74
  3. Prototype Based Classification

  4. Classification and Non-Standard Metrics

    1. Front Matter
      Pages 119-119
    2. Barbara Hammer, David Nebel, Martin Riedel, Thomas Villmann
      Pages 123-132
    3. Sven Hellbach, Marian Himstedt, Frank Bahrmann, Martin Riedel, Thomas Villmann, Hans-Joachim Böhme
      Pages 133-143
    4. Anastasios Bellas, Charles Bouveyron, Marie Cottrell, Jerome Lacaille
      Pages 145-155
    5. Mathias Klingner, Sven Hellbach, Martin Riedel, Marika Kaden, Thomas Villmann, Hans-Joachim Böhme
      Pages 157-166

About these proceedings

Introduction

The book collects the scientific contributions presented at the 10th Workshop on Self-Organizing Maps (WSOM 2014) held at the University of Applied Sciences Mittweida, Mittweida (Germany, Saxony), on July 2–4, 2014. Starting with the first WSOM-workshop 1997 in Helsinki this workshop focuses on newest results in the field of supervised and unsupervised vector quantization like self-organizing maps for data mining and data classification.  

This 10th WSOM brought together more than 50 researchers, experts and practitioners in the beautiful small town Mittweida in Saxony (Germany) nearby the mountains Erzgebirge to discuss new developments in the field of unsupervised self-organizing vector quantization systems and learning vector quantization approaches for classification. The book contains the accepted papers of the workshop after a careful review process as well as summaries of the invited talks.   Among these book chapters there are excellent examples of the use of self-organizing maps in agriculture, computer science, data visualization, health systems, economics, engineering, social sciences, text and image analysis, and time series analysis. Other chapters present the latest theoretical work on self-organizing maps as well as learning vector quantization methods, such as relating those methods to classical statistical decision methods.

All the contribution demonstrate that vector quantization methods cover a large range of application areas including data visualization of high-dimensional complex data, advanced decision making and classification or data clustering and data compression.

Keywords

Intelligent Systems Learning Vector Quantization Self-Organizing Maps

Editors and affiliations

  • Thomas Villmann
    • 1
  • Frank-Michael Schleif
    • 2
  • Marika Kaden
    • 3
  • Mandy Lange
    • 4
  1. 1.Department of MathematicsUniversity of Applied Sciences MittweidaMittweidaGermany
  2. 2.University of Applied Sciences MittweidaMittweidaGermany
  3. 3.University of Applied Sciences MittweidaMittweidaGermany
  4. 4.University of Applied Sciences MittweidaMittweidaGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-07695-9
  • Copyright Information Springer International Publishing Switzerland 2014
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-07694-2
  • Online ISBN 978-3-319-07695-9
  • Series Print ISSN 2194-5357
  • Series Online ISSN 2194-5365
  • About this book