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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

  • Conference proceedings
  • © 2020

Overview

Editors:
  1. Alfredo Vellido

    1. Department of Computer Science, UPC BarcelonaTech, Barcelona, Spain

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  2. Karina Gibert

    1. Knowledge Engineering and Machine Learning Group (KEMLG) at Intelligent Data Science and Artificial Intelligence Research Center, UPC BarcelonaTech, Barcelona, Spain

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  3. Cecilio Angulo

    1. Department of Automatic Control, UPC BarcelonaTech, Barcelona, Spain

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  4. José David Martín Guerrero

    1. Departament d'Enginyeria Electrònica, Universitat de València, Burjassot, Spain

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  • Covers the latest theoretical developments in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization
  • Presents computational aspects and applications for data mining and visualization
  • Gathers refereed papers presented at the 13th International Workshop WSOM+ 2019, held in Barcelona, Spain on 26–28 June 2019

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

Included in the following conference series:

Conference proceedings info: WSOM 2019.

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Table of contents (33 papers)

  1. Front Matter

    Pages i-xii
    Download chapter PDF

  2. Self-organizing Maps: Theoretical Developments

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Look and Feel What and How Recurrent Self-Organizing Maps Learn

      • Jérémy Fix, Hervé Frezza-Buet
      Pages 3-12

    3. Self-Organizing Mappings on the Flag Manifold

      • Xiaofeng Ma, Michael Kirby, Chris Peterson
      Pages 13-22

    4. Self-Organizing Maps with Convolutional Layers

      • Lars Elend, Oliver Kramer
      Pages 23-32

    5. Cellular Self-Organising Maps - CSOM

      • Bernard Girau, Andres Upegui
      Pages 33-43

    6. A Probabilistic Method for Pruning CADJ Graphs with Applications to SOM Clustering

      • Josh Taylor, Erzsébet Merényi
      Pages 44-54

  3. Practical Applications of Self-Organizing Maps, Learning Vector Quantization and Clustering

    1. Front Matter

      Pages 55-55
      Download chapter PDF

    2. SOM-Based Anomaly Detection and Localization for Space Subsystems

      • Maia Rosengarten, Sowmya Ramachandran
      Pages 57-69

    3. Self-Organizing Maps in Earth Observation Data Cubes Analysis

      • Lorena Santos, Karine Reis Ferreira, Michelle Picoli, Gilberto Camara
      Pages 70-79

    4. Competencies in Higher Education: A Feature Analysis with Self-Organizing Maps

      • Alberto Nogales, Álvaro José García-Tejedor, Noemy Martín Sanz, Teresa de Dios Alija
      Pages 80-89

    5. Using SOM-Based Visualization to Analyze the Financial Performance of Consumer Discretionary Firms

      • Zefeng Bai, Nitin Jain, Ying Wang, Dominique Haughton
      Pages 90-99

    6. Novelty Detection with Self-Organizing Maps for Autonomous Extraction of Salient Tracking Features

      • Yann Bernard, Nicolas Hueber, Bernard Girau
      Pages 100-109

    7. Robust Adaptive SOMs Challenges in a Varied Datasets Analytics

      • Alaa Ali Hameed, Naim Ajlouni, Bekir Karlik
      Pages 110-119

    8. Detection of Abnormal Flights Using Fickle Instances in SOM Maps

      • Marie Cottrell, Cynthia Faure, Jérôme Lacaille, Madalina Olteanu
      Pages 120-129

    9. LVQ-type Classifiers for Condition Monitoring of Induction Motors: A Performance Comparison

      • Diego P. Sousa, Guilherme A. Barreto, Charles C. Cavalcante, Cláudio M. S. Medeiros
      Pages 130-139

    10. When Clustering the Multiscalar Fingerprint of the City Reveals Its Segregation Patterns

      • Madalina Olteanu, Jean-Charles Lamirel
      Pages 140-149

    11. Using Hierarchical Clustering to Understand Behavior of 3D Printer Sensors

      • Ashutosh Karna, Karina Gibert
      Pages 150-159

    12. A Walk Through Spectral Bands: Using Virtual Reality to Better Visualize Hyperspectral Data

      • Henry Kvinge, Michael Kirby, Chris Peterson, Chad Eitel, Tod Clapp
      Pages 160-165

    13. Incremental Traversability Assessment Learning Using Growing Neural Gas Algorithm

      • Jan Faigl, Miloš Prágr
      Pages 166-176
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  1. Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization

Keywords

About this book

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.

Editors and Affiliations

  • Department of Computer Science, UPC BarcelonaTech, Barcelona, Spain

    Alfredo Vellido

  • Knowledge Engineering and Machine Learning Group (KEMLG) at Intelligent Data Science and Artificial Intelligence Research Center, UPC BarcelonaTech, Barcelona, Spain

    Karina Gibert

  • Department of Automatic Control, UPC BarcelonaTech, Barcelona, Spain

    Cecilio Angulo

  • Departament d'Enginyeria Electrònica, Universitat de València, Burjassot, Spain

    José David Martín Guerrero

Bibliographic Information

  • Book Title: Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization

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

  • Editors: Alfredo Vellido, Karina Gibert, Cecilio Angulo, José David Martín Guerrero

  • Series Title: Advances in Intelligent Systems and Computing

  • DOI: https://doi.org/10.1007/978-3-030-19642-4

  • Publisher: Springer Cham

  • eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)

  • Copyright Information: Springer Nature Switzerland AG 2020

  • Softcover ISBN: 978-3-030-19641-7Published: 28 April 2019

  • eBook ISBN: 978-3-030-19642-4Published: 27 April 2019

  • Series ISSN: 2194-5357

  • Series E-ISSN: 2194-5365

  • Edition Number: 1

  • Number of Pages: XII, 342

  • Number of Illustrations: 48 b/w illustrations, 113 illustrations in colour

  • Topics: Computational Intelligence, Artificial Intelligence

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