Advances in Self-Organising Maps

1. Front Matter

   Pages I-IX

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2. Towards an information-theoretic approach to kernel-based topographic map formation

   • Marc M. Van Hulle

   Pages 1-6

3. A Statistical Tool to Assess the Reliability of Self-Organizing Maps

   • M. Cottrell, E. de Bodt, M. Verleysen

   Pages 7-14

4. A SOM Association Network

   • T. Yamakawa, K. Horio, R. Kubota

   Pages 15-20

5. A Supervised Self-Organizing Map for Structured Data

   • Markus Hagenbuchner, Ah Chung Tsoi, Alessandro Sperduti

   Pages 21-28

6. Human Gait Analysis using SOM*

   • H. M. Lakany

   Pages 29-38

7. Exploring Financial Crises Data with Self-Organizing Maps (SOM)

   • Ismael Arciniegas, Betty Daniel, Mark J Embrechts

   Pages 39-46

8. Analysing Health Inequalities Using SOM

   • Zheng Rong Yang

   Pages 47-53

9. Integrating Contextual Information into Text Document Clustering with Self-Organizing Maps

   • Daniel Pullwitt, Ralf Der

   Pages 54-60

10. Self-Organizing Internet Semantic Network

    • Sergey Shumsky

    Pages 61-66

11. Recursive learning rules for SOMs

    • John A. Lee, Nicolas Donckers, Michel Verleysen

    Pages 67-72

12. Induced Voronoï Kernels for Principal Manifolds Approximation

    • Michaël Aupetit, Pierre Couturier, Pierre Massotte

    Pages 73-80

13. Visualisation Induced SOM (ViSOM)

    • Hujun Yin

    Pages 81-88

14. An approach to automated interpretation of SOM

    • Markus Siponen, Juha Vesanto, Olli Simula, Petri Vasara

    Pages 89-94

15. VQ-Based Clustering Algorithm of Piecewise- Dependent-Data

    • Itshak Lapidot, Hugo Guterman

    Pages 95-101

16. Adaptive Subspace Encoders Using Stochastic Vector Quantisers

    • S P Luttrell

    Pages 102-109

17. Shapesom

    • P. N. Suganthan

    Pages 110-117

18. A New Interpolation Algorithm Employing a Self-Organizing Map

    • T. Yamakawa, K. Horio, Y. Oosako, T. Miki

    Pages 118-123

19. SOM-Based Exploratory Analysis of Gene Expression Data

    • Samuel Kaski

    Pages 124-131

20. Exploring power transformer database using Self-Organising Maps (SOM) and Minimal Spanning Tree (MST)

    • K. Obu-Cann, K. Fujimura, H. Tokutaka, M. Ohkita, M. Inui, S. Yamada

    Pages 132-139

This is the third Workshop on Self-Organising Maps (WSOM) and its related techniques. The previous two were held in Helsinki (1997 and 1999) and confIrmed the vitality of the SOM as one of the most popular and powerful concepts for unsupervised pattern recognition and data visualisation. These meetings not only acted as a showcase for the latest advances in SOM theory and for illustrating its vast range of applicability, but also as venues where much informal and fruitful interaction could take place. It is interesting to observe the development of the original SOM, and this remarkable progress confrrms the originality and insight of Teuvo Kohonen's pioneering work. With the range and quality of the papers in this volume, the stage is set for another very successful meeting. This volume is a permanent record of all the contributions presented during WSOM'OI held at the University of Lincolnshire and Humberside, 13 - 15 June, 2001. The University is the newest of England's universities but it is situated in the heart of one of our oldest cities - founded by the Romans and overlooked by the towering mass of its medieval cathedral. Primarily Lincoln has always been a centre for the rich agricultural heartland of England; however, it was the birthplace, 186 years ago, of George Boole. So WSOM'OI is continuing Lincoln's long and honourable tradition of advancing scientific understanding.

• Data mining and visualisation
• Image Processing
• STATISTICA
• Signal Processing
• algorithms
• classification
• optimization
• pattern recognition

• Department of Electrical Engineering and Electronics, UMIST, Manchester, England

  Nigel Allinson, Hujun Yin

• Department of Psychology, University of Lincolnshire and Humberside, Lincoln, England

  Lesley Allinson, Jon Slack

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