Advances in Self-Organizing Maps

1. Front Matter

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2. Batch-Learning Self-Organizing Map for Predicting Functions of Poorly-Characterized Proteins Massively Accumulated

   • Takashi Abe, Shigehiko Kanaya, Toshimichi Ikemura

   Pages 1-9

3. Incremental Unsupervised Time Series Analysis Using Merge Growing Neural Gas

   • Andreas Andreakis, Nicolai v. Hoyningen-Huene, Michael Beetz

   Pages 10-18

4. Clustering Hierarchical Data Using Self-Organizing Map: A Graph-Theoretical Approach

   • Argyris Argyrou

   Pages 19-27

5. Time Series Clustering for Anomaly Detection Using Competitive Neural Networks

   • Guilherme A. Barreto, Leonardo Aguayo

   Pages 28-36

6. Fault Prediction in Aircraft Engines Using Self-Organizing Maps

   • Marie Cottrell, Patrice Gaubert, Cédric Eloy, Damien François, Geoffroy Hallaux, Jérôme Lacaille et al.

   Pages 37-44

7. Incremental Figure-Ground Segmentation Using Localized Adaptive Metrics in LVQ

   • Alexander Denecke, Heiko Wersing, Jochen J. Steil, Edgar Körner

   Pages 45-53

8. Application of Supervised Pareto Learning Self Organizing Maps and Its Incremental Learning

   • Hiroshi Dozono, Shigeomi Hara, Shinsuke Itou, Masanori Nakakuni

   Pages 54-62

9. Gamma SOM for Temporal Sequence Processing

   • Pablo A. Estévez, Rodrigo Hernández

   Pages 63-71

10. Fuzzy Variant of Affinity Propagation in Comparison to Median Fuzzy c-Means

    • T. Geweniger, D. Zühlke, B. Hammer, Thomas Villmann

    Pages 72-79

11. Clustering with Swarm Algorithms Compared to Emergent SOM

    • Lutz Herrmann, Alfred Ultsch

    Pages 80-88

12. Cartograms, Self-Organizing Maps, and Magnification Control

    • Roberto Henriques, Fernando Bação, Victor Lobo

    Pages 89-97

13. Concept Mining with Self-Organizing Maps for the Semantic Web

    • Timo Honkela, Matti Pöllä

    Pages 98-106

14. ViSOM for Dimensionality Reduction in Face Recognition

    • Weilin Huang, Hujun Yin

    Pages 107-115

15. Early Recognition of Gesture Patterns Using Sparse Code of Self-Organizing Map

    • Manabu Kawashima, Atsushi Shimada, Rin-ichiro Taniguchi

    Pages 116-123

16. Bag-of-Features Codebook Generation by Self-Organisation

    • Teemu Kinnunen, Joni-Kristian Kamarainen, Lasse Lensu, Heikki Kälviäinen

    Pages 124-132

17. On the Quantization Error in SOM vs. VQ: A Critical and Systematic Study

    • Teuvo Kohonen, Ilari T. Nieminen, Timo Honkela

    Pages 133-144

18. Approaching the Time Dependent Cocktail Party Problem with Online Sparse Coding Neural Gas

    • Kai Labusch, Erhardt Barth, Thomas Martinetz

    Pages 145-153

19. Career-Path Analysis Using Optimal Matching and Self-Organizing Maps

    • Sébastien Massoni, Madalina Olteanu, Patrick Rousset

    Pages 154-162

20. Network-Structured Particle Swarm Optimizer with Various Topology and Its Behaviors

    • Haruna Matsushita, Yoshifumi Nishio

    Pages 163-171

th These proceedings contain refereed papers presented at the 7 WSOM held at the Casa Monica Hotel, St. Augustine, Florida, June 8–10, 2009. We designed the wo- shop to serve as a regular forum for researchers in academia and industry who are interested in the exciting field of self-organizing maps (SOM). The program includes excellent examples of the use of SOM in many areas of social sciences, economics, computational biology, engineering, time series analysis, data visualization and c- puter science as well a vibrant set of theoretical papers that keep pushing the envelope of the original SOM. Our deep appreciation is extended to Teuvo Kohonen and Ping Li for the plenary talks and Amaury Lendasse for the organization of the special sessions. Our sincere thanks go to the members of the Technical Committee and other reviewers for their excellent and timely reviews, and above all to the authors whose contributions made this workshop possible. Special thanks go to Julie Veal for her dedication and hard work in coordinating the many details necessary to put together the program and local arrangements. Jose C. Principe Risto Miikkulainen

• aircraft engines
• algorithms
• classification
• clustering
• data mining
• dimensionality reduction
• fault prediction
• handwriting
• model
• modeling
• ocr
• proteins
• self-organizing map
• supervised learning
• topology
• algorithm analysis and problem complexity

• Computational NeuroEngineering Laboratory, University of Florida,, Gainesville, USA

  José C. Príncipe

• Department of Computer Sciences, The University of Texas at Austin, Austin, USA

  Risto Miikkulainen

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