Advances in Self-Organizing Maps and Learning Vector Quantization

1. Front Matter

   Pages i-xiii

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2. Self-Organizing Map Learning, Visualization, and Quality Assessment

   1. Front Matter

      Pages 1-1

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   2. Theoretical and Applied Aspects of the Self-Organizing Maps

      • Marie Cottrell, Madalina Olteanu, Fabrice Rossi, Nathalie Villa-Vialaneix

      Pages 3-26

   3. Aggregating Self-Organizing Maps with Topology Preservation

      • Jérôme Mariette, Nathalie Villa-Vialaneix

      Pages 27-37

   4. ESOM Visualizations for Quality Assessment in Clustering

      • Alfred Ultsch, Martin Behnisch, Jörn Lötsch

      Pages 39-48

   5. SOM Quality Measures: An Efficient Statistical Approach

      • Lutz Hamel

      Pages 49-59

   6. SOM Training Optimization Using Triangle Inequality

      • Denny, William Gozali, Ruli Manurung

      Pages 61-71

   7. Sparse Online Self-Organizing Maps for Large Relational Data

      • Madalina Olteanu, Nathalie Villa-Vialaneix

      Pages 73-82

3. Clustering and Time Series Analysis with Self-Organizing Maps and Neural Gas

   1. Front Matter

      Pages 83-83

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   2. A Neural Gas Based Approximate Spectral Clustering Ensemble

      • Yaser Moazzen, Kadim Taşdemir

      Pages 85-93

   3. Reliable Clustering Quality Estimation from Low to High Dimensional Data

      • Jean-Charles Lamirel

      Pages 95-105

   4. Segment Growing Neural Gas for Nonlinear Time Series Analysis

      • Jorge R. Vergara, Pablo A. Estévez, Álvaro Serrano

      Pages 107-117

   5. Modeling Diversity in Ensembles for Time-Series Prediction Based on Self-Organizing Maps

      • Rigoberto Fonseca-Delgado, Pilar Gómez-Gil

      Pages 119-128

4. Applications in Control, Planning, and Dimensionality Reduction, and Hardware for Self-Organizing Maps

   1. Front Matter

      Pages 129-129

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   2. Modular Self-Organizing Control for Linear and Nonlinear Systems

      • Paulo Henrique Muniz Ferreira, Aluízio Fausto Ribeiro Araújo

      Pages 131-141

   3. On Self-Organizing Map and Rapidly-Exploring Random Graph in Multi-Goal Planning

      • Jan Faigl

      Pages 143-153

   4. Dimensionality Reduction Hybridizations with Multi-dimensional Scaling

      • Oliver Kramer

      Pages 155-163

   5. A Scalable Flexible SOM NoC-Based Hardware Architecture

      • Mehdi Abadi, Slavisa Jovanovic, Khaled Ben Khalifa, Serge Weber, Mohamed Hédi Bedoui

      Pages 165-175

   6. Local Models for Learning Inverse Kinematics of Redundant Robots: A Performance Comparison

      • Humberto I. Fontinele, Davyd B. Melo, Guilherme A. Barreto

      Pages 177-187

5. Self-Organizing Maps in Neuroscience and Medical Applications

   1. Front Matter

      Pages 189-189

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This book contains the articles from the international conference 11th Workshop on Self-Organizing Maps 2016 (WSOM 2016), held at Rice University in Houston, Texas, 6-8 January 2016. WSOM is a biennial international conference series starting with WSOM'97 in Helsinki, Finland, under the guidance and direction of Professor Tuevo Kohonen (Emeritus Professor, Academy of Finland). WSOM brings together the state-of-the-art theory and applications in Competitive Learning Neural Networks: SOMs, LVQs and related paradigms of unsupervised and supervised vector quantization.
The current proceedings present the expert body of knowledge of 93 authors from 15 countries in 31 peer reviewed contributions. It includes papers and abstracts from the WSOM 2016 invited speakers representing leading researchers in the theory and real-world applications of Self-Organizing Maps and Learning Vector Quantization: Professor Marie Cottrell (Universite Paris 1 Pantheon Sorbonne, France), Professor Pablo Estevez (University of Chile and Millennium Instituteof Astrophysics, Chile), and Professor Risto Miikkulainen (University of Texas at Austin, USA). The book comprises a diverse set of theoretical works on Self-Organizing Maps, Neural Gas, Learning Vector Quantization and related topics, and an excellent variety of applications to data visualization, clustering, classification, language processing, robotic control, planning, and to the analysis of astronomical data, brain images, clinical data, time series, and agricultural data.

• Computational Intelligence
• Intelligent Systems
• LVQ
• Learning Vector Quantization
• SOM
• Self-Organizing Maps
• WSOM 2016

• Department of Statistics, Rice University, Houston, USA

  Erzsébet Merényi

• Department of Electrical and Computer En, Air Force Institute of Technology, Wright-Patterson AFB, USA

  Michael J. Mendenhall

• Applied Physics, Rice University, Houston, USA

  Patrick O'Driscoll

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