Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization

Proceedings of the 13th International Workshop, WSOM+ 2019, Barcelona, Spain, June 26-28, 2019

  • Alfredo Vellido
  • Karina Gibert
  • Cecilio Angulo
  • José David Martín Guerrero
Conference proceedings WSOM 2019

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 976)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Self-organizing Maps: Theoretical Developments

    1. Front Matter
      Pages 1-1
    2. Jérémy Fix, Hervé Frezza-Buet
      Pages 3-12
    3. Xiaofeng Ma, Michael Kirby, Chris Peterson
      Pages 13-22
    4. Lars Elend, Oliver Kramer
      Pages 23-32
    5. Bernard Girau, Andres Upegui
      Pages 33-43
  3. Practical Applications of Self-Organizing Maps, Learning Vector Quantization and Clustering

    1. Front Matter
      Pages 55-55
    2. Maia Rosengarten, Sowmya Ramachandran
      Pages 57-69
    3. Lorena Santos, Karine Reis Ferreira, Michelle Picoli, Gilberto Camara
      Pages 70-79
    4. Alberto Nogales, Álvaro José García-Tejedor, Noemy Martín Sanz, Teresa de Dios Alija
      Pages 80-89
    5. Zefeng Bai, Nitin Jain, Ying Wang, Dominique Haughton
      Pages 90-99
    6. Alaa Ali Hameed, Naim Ajlouni, Bekir Karlik
      Pages 110-119
    7. Marie Cottrell, Cynthia Faure, Jérôme Lacaille, Madalina Olteanu
      Pages 120-129
    8. Diego P. Sousa, Guilherme A. Barreto, Charles C. Cavalcante, Cláudio M. S. Medeiros
      Pages 130-139
    9. Henry Kvinge, Michael Kirby, Chris Peterson, Chad Eitel, Tod Clapp
      Pages 160-165
  4. Learning Vector Quantization: Theoretical Developments

    1. Front Matter
      Pages 177-177
    2. Thomas Villmann, Jensun Ravichandran, Andrea Villmann, David Nebel, Marika Kaden
      Pages 179-188
    3. Sascha Saralajew, Lars Holdijk, Maike Rees, Thomas Villmann
      Pages 189-199
    4. Moritz Heusinger, Christoph Raab, Frank-Michael Schleif
      Pages 200-209
    5. Michael Biehl, Fthi Abadi, Christina Göpfert, Barbara Hammer
      Pages 210-221
  5. Theoretical Developments in Clustering, Deep Learning and Neural Gas

    1. Front Matter
      Pages 223-223
    2. Mohammed Oualid Attaoui, Mustapha Lebbah, Nabil Keskes, Hanene Azzag, Mohammed Ghesmoune
      Pages 225-230
    3. Shannon Stiverson, Michael Kirby, Chris Peterson
      Pages 251-260
    4. Tina Geweniger, Thomas Villmann
      Pages 261-270
    5. Rudolf Szadkowski, Jan Drchal, Jan Faigl
      Pages 271-281
  6. Life Science Applications

    1. Front Matter
      Pages 283-283
    2. Patrick Riley, Ivan Olier, Marc Rea, Paulo Lisboa, Sandra Ortega-Martorell
      Pages 294-303
    3. Meenal Srivastava, Ivan Olier, Patrick Riley, Paulo Lisboa, Sandra Ortega-Martorell
      Pages 304-313
    4. Thomas Villmann, Marika Kaden, Szymon Wasik, Mateusz Kudla, Kaja Gutowska, Andrea Villmann et al.
      Pages 324-333
    5. Niina Gen, Tokutaka Heizo, Ohkita Masaaki, Kasezawa Nobuhiko
      Pages 334-339
  7. Back Matter
    Pages 341-342

About these proceedings


This book gathers papers presented at the 13th International Workshop on Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization (WSOM+), which was held in Barcelona, Spain, from the 26th to the 28th of June 2019. Since being founded in 1997, the conference has showcased the state of the art in unsupervised machine learning methods related to the successful and widely used self-organizing map (SOM) method, and extending its scope to clustering and data visualization. In this installment of the AISC series, the reader will find theoretical research on SOM, LVQ and related methods, as well as numerous applications to problems in fields ranging from business and engineering to the life sciences. Given the scope of its coverage, the book will be of interest to machine learning researchers and practitioners in general and, more specifically, to those looking for the latest developments in unsupervised learning and data visualization.


Computational Intelligence Intelligent Systems LVQ Learning Vector Quantization SOM Self-Organizing Maps Data Visualization WSOM WSOM 2019

Editors and affiliations

  • Alfredo Vellido
    • 1
  • Karina Gibert
    • 2
  • Cecilio Angulo
    • 3
  • José David Martín Guerrero
    • 4
  1. 1.Department of Computer ScienceUPC BarcelonaTechBarcelonaSpain
  2. 2.Knowledge Engineering and Machine Learning Group (KEMLG) at Intelligent Data Science and Artificial Intelligence Research CenterUPC BarcelonaTechBarcelonaSpain
  3. 3.Department of Automatic ControlUPC BarcelonaTechBarcelonaSpain
  4. 4.Departament d'Enginyeria ElectrònicaUniversitat de ValènciaBurjassotSpain

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