SOM-based data visualization methods

Intelligent Data Analysis

Volumn 3, Issue 2, 1999, Pages 111-126

  Vesanto, Juha ^a  

^a AALTO UNIVERSITY   (Finland)

Author keywords

Data mining; Projection; Self organizing map; Vector quantization; Visualization

Indexed keywords

CONFORMAL MAPPING; DATA MINING; FLOW VISUALIZATION; SELF ORGANIZING MAPS; VECTOR QUANTIZATION; VISUALIZATION;

K-MEANS; NUMERICAL DATA; PROJECTION; VECTOR PROJECTION; VISUALIZATION METHOD;

DATA VISUALIZATION;

EID: 38049168357     PISSN: 1088467X     EISSN: 15714128     Source Type: Journal    
DOI: 10.3233/IDA-1999-3203     Document Type: Article

References (27)

1. C.M. Bishop, Neural Networks for Pattern recognition, Oxford University Press, Oxford, 1995.
2. G. Deboeck, T. Kohonen (Eds.), Visual Explorations in Finance using self-Organizing Maps, Springer, London, 1998.
3. U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, R. Uthurusamy (Eds.), Advances in Knowledge Discovery and Data Mining, AAAI Press/The MIT Press, California, 1996.
4. E. Hiikkinen, P. Koikkalainen, The neural data analysis environment, in: Proceedings of the Workshop on Self-Organizing Map, 1997, pp. 69-74.
5. T. Hastie, W. Stuetzle, Principal curves, Journal of the American Statistical Association 84 (1989) 502-516.
6. J. Himberg, Enhancing the SOM-based data visualization by linking different data projections, in: Proceedings of the International Symposium on Intelligent Data Engineering and Learning (IDEAL), Hong Kong, 1998, pp. 427-434.
7. L. Holmstrom, A. Hiimiiliiinen, The self-organizing reduced kernel density estimator, in: Proceedings of the IEEE International Conference on Neural Networks (ICNN'93), San Francisco, CA, 1993, pp. 417-421.
8. J. Iivarinen, T. Kohonen, J. Kangas, S. Kaski, Visualizing the clusters on the self-organizing map, in: C. Carlsson, T. Jiirvi, T. Reponen (Eds.), Proceedings of the Conference on Artificial Intelligence Research in Finland, Conference Proceedings of Finnish Artificial Intelligence Society, Finnish Artificial Intelligence Society, vol. 12, Helsinki, Finland, 1994, pp. 122-126.
9. S. Kaski, Data Exploration Using Self-Organizing Maps, Ph.D thesis, Helsinki University of Technology, 1997.
10. S. Kaski, T. Kohonen, J. Venna, Tips for SOM Processing and Colorcoding of Maps, in: G. Deboeck, T. Kohonen (Eds.), Visual Explorations in Finance using Self-Organizing Maps, Springer, London, 1998.
11. S Kaski, L. Nikkilii, T. Kohonen, Methods for interpreting a self-organized map in data analysis, in: Proceedings of European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium, 1998.
12. T. Kohonen, Self-Organizing Maps, Springer, Berlin, 1995.
13. T. Kohonen, Simula, A. Visa, J. Kangas, Engineering applications of the self-organizing map, Proceedings of the IEEE 84 (10) (1996) 27.
14. A. Konig, A survey of methods for multivariate data projection, visualization and interactive analysis, in: Proceedings of the Fifth International Conference on Soft Computing and Information/Intelligent Systems (IIZUKA'98), 1998, pp. 55-59.
15. A. Konig, O. Bulmahn, M. Glesner, Systematic methods for multivariate data visualization and numerical assessment of class separability and overlap in automated visual industrial control, in: Proceedings of the Fifth British Machine Vision Conference (BMVC'94), 1994, pp. 195-204.
16. M.A. Kraaijveld, J. Mao, A.K. Jain, A nonlinear projection method based on Kohonen's topology preserving maps, IEEE Transactions on Neural Networks 6 (3) (1995) 548-559.
17. J. Mao, AK. Jain, Artificial neural networks for feature extraction and multivariate data projection, IEEE Transactions on Neural Networks 6 (2) (1995) 296-317.
18. D. Merkl, A Rauber, Alternative ways for cluster visualization in self-organizing maps, in: Proceedings of the Workshop on SelfOrganizing Map, 1997, pp. 106-111.
19. D.S. Modha, S. Spangler, S. Vaithyanathan, Multidimensional cluster visualization using guided tours, Technical Report Research Report RJ 10124, IBM Almaden Research Center, San Jose, CA, 1998. http://www.alphaWorks.ibm.com/ formula/Cviz.
20. W. Pedrycz, H.C. Card, Linguistic interpretation of self-organizing maps, in: Proceedings of International Conference on Fuzzy Systems '92, 1992.
21. B.D. Ripley, Pattern Recognition and Neural Networks, Cambridge University Press, Cambridge, 1996.
22. W. Siedlecki, K. Siedlecka, J. Sklansky, An overview of mapping techniques for exploratory pattern analysis, Pattern Recognition 21 (5) (1988) pp. 411-429.
23. O. Simula, J. Kangas, Process monitoring and visualization using self-organizing maps, in: Neural Networks for Chemical Engineers, Computer-Aided Chemical Engineering, vol 6, Elsevier, Amsterdam, 1995.
24. V. Tryba, K. Goser, Self-organizing feature maps for process control in chemistry, in: T. Kohonen, K. Miikisara, O. Simula, J. Kangas (Eds.), Artificial Neural Networks, Amsterdam, Netherlands, 1991, pp. 847-852.
25. A Ultsch, Self organized feature maps for monitoring and knowledge acquisition of a chemical process, in: Proceedings of International Conference on Artificial Neural Networks (lCANN'93), Springer, London, 1993, pp. 864-867.
26. A. Ultsch, H.P. Siemon, Kohonen's self organizing feature maps for exploratory data analysis, in: Proceedings of International Neural Network Conference (INNC'90), Kluwer academic Publishers, Dordrecht, 1990, pp. 305-308.
27. J. Vesanto, Data mining techniques based on the self-organizing map, Master's thesis, Helsinki University of Technology, June 1997. http ://www.cis.hut.fi/proj ects/ide/publications/.