Data exploration using self-organizing maps

Acta Polytechnica Scandinavica Mathematics and Computing Series

Volumn 82, Issue , 1997, Pages

  Kaski, Samuel ^a  

^a AALTO UNIVERSITY   (Finland)

Author keywords

Data mining; Exploratory data analysis; Multivariate analysis; Neural networks; Self organizing map; SOM

Indexed keywords

EID: 13844291291     PISSN: 14569418     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (30)

1. Alander, J. T., Frisk, M., Holmström, L., Hämäläinen, A., and Tuominen, J. (1991) Process error detection using self-organizing feature maps. In Kohonen, T., Mäkisara, K., Simula, O., and Kangas, J., editors, Artificial Neural Networks. Proceedings of ICANN'91, International Conference on Artificial Neural Networks, volume II, pages 1229-1232. North-Holland, Amsterdam.
2. Amari, S. (1980) Topographic organization of nerve fields. Bulletin of Mathematical Biology, 42:339-364.
3. Anderberg, M. R. (1973) Cluster Analysis for Applications. Academic Press, New York, NY.
4. Andrews, D. F. (1972) Plots of high-dimensional data. Biometrics, 28:125-136.
5. Angéniol, B., de La Croix Vaubois, G., and Le Texier, J.-Y. (1988). Selforganizing feature maps and the traveling salesman problem. Neural Networks, 1:289-293.
6. Back, B., Sere, K., and Vanharanta, H. (1996) Data mining accounting numbers using self-organizing maps. In Alander, J., Honkela, T., and Jakobsson, M., editors, Proceedings of STeP'96, Finnish Artificial Intelligence Conference, pages 35-47. Finnish Artificial Intelligence Society, Vaasa, Finland.
7. Bellman, R. E. (1961) Adaptive Control Processes: A Guided Tour, Princeton University Press, New Jersey, NJ.
8. Bezdek, J. C. and Pal, N. R. (1995) An index of topological preservation for feature extraction. Pattern Recognition, 28:381-391.
9. Bishop, C. M., Svensén, M., and Williams, C. K. I. (1996a). EM optimization of latent-variable models. In Touretzky, D. S., Mozer, M. C., and Hasselmo, M. E., editors, Advances in Neural Information Processing Systems 8, pages 465-471. The MIT Press, Cambridge, MA.
10. Bishop, C. M., Svensén, M., and Williams, C. K. I. (1996b). GTM: a principled alternative to the self-organizing map. In von der Malsburg, C., von Seelen, W., Vorbrüggen, J. C., and Sendhoff, B., editors, Proceedings of ICANN'96, International Conference on Artificial Neural Networks, Lecture Notes in Computer Science, vol. 1112, pages 165-170. Springer, Berlin.
11. Biswas, G., Jain, A. K., and Dubes, R. C. (1981) Evaluation of projection algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 3:701-708.
12. Blackmore, J. and Miikkulainen, R. (1993) Incremental grid growing: encoding high-dimensional structure into a two-dimensional feature map. In Proceedings of ICNN'93, IEEE International Conference on Neural Networks, volume I, pages 450-455. IEEE Service Center, Piscataway, NJ.
13. Blayo, F. and Demartines, P. (1992) Algorithme de Kolionen: application à l'analyse de données économiques. Bulletin des Schweizerischen Elektrotechnischen Vereins & des Verbundes Schweizerischer Elektrizitätswerke, 83(5):23-26.
14. Bruske, J. and Sommer, G. (1995) Dynamic cell structure learns perfectly topology preserving map. Neural Computation, 7:845-865.
15. Budinich, M. (1996). A self-organizing neural network for the traveling salesman problem that is competitive with simulated annealing. Neural Computation, 8:416-424.
16. Carlson, E. (1991) Self-organizing feature maps for appraisal of land value of shore parcels. In Kohonen, T., Mäkisara, K., Simula, O., and Kangas, J., editors, Artificial Neural Networks. Proceedings of ICANN'91, International Conference on Artificial Neural Networks, volume II, pages 1309-1312, North-Holland, Amsterdam.
17. Chang, C. L. and Lee, R. C. T. (1973) A heuristic relaxation method for nonlinear mapping in cluster analysis. IEEE Transactions on Systems, Man, and Cybernetics, 3:197-200.
18. Cheng, G., Liu, X., and Wu, J. X. (1994) Interactive knowledge discovery through self-organizing feature maps. In Proceedings of WCNN'94, World Congress on Neural Networks, volume IV, pages 430-434. Lawrence Erlbaum, Hillsdale, NJ.
19. Chernoff, H. (1973) The use of faces to represent points in k-dimensional space graphically. Journal of the American Statistical Association, 68:361-368.
20. Cichocki, A. and Unbehauen, R. (1993) Neural Networks for Optimization and Signal Processing. John Wiley, Chichester, England.
21. Cooley, W. W. and Lohnes, P. R. (1971) Multivariate Data Analysis. Wiley, New York, NY.
22. de Leeuw, J. and Heiser, W. (1982) Theory of multidimensional scaling. In Krishnaiah, P. R. and Kanal, L. N., editors, Handbook of Statistics, volume 2, pages 285-316. North-Holland, Amsterdam.
23. Demartines, P. (1994) Analyse de données par réseaux de neurones auto-organisés (Data analysis through self-organized neural networks). PhD thesis, Institut National Polytechnique de Grenoble, Grenoble, France.
24. Wish, M. and Carroll, J. D. (1982) Multidimensional scaling and its applications. In Krishnaiah, P. R. and Kanal, L. N., editors, Handbook of Statistics, volume 2, pages 317-345. North-Holland, Amsterdam.
25. World Bank (1992) World Development Report 1992. Oxford University Press, New York, NY.
26. Young, F. W. (1985) Multidimensional scaling. In Kotz, S., Johnson, N. L., and Read, C. B., editors, Encyclopedia of Statistical Sciences, volume 5, pages 649-659. Wiley, New York, NY.
27. Young, G. and Householder, A. S. (1938). Discussion of a set of points in terms of their mutual distances. Psychometrika, 3:19-22 .
28. Zador, P. L. (1982). Asymptotic quantization error of continuous signals and the quantization dimension. IEEE Transactions on Information Theory, 28:139-149.
29. Zhang, X. and Li, Y. (1993) Self-organizing map as a new method for clustering and data analysis. In Proceedings of IJCNN'93 (Nagoya), International Joint Conference on Neural Networks, pages 2448-2451. IEEE Service Center, Piscataway, NJ.
30. Zrehen, S. (1993) Analyzing Kohonen maps with geometry. In Gielen, S. and Kappen, B., editors, Proceedings of ICANN'93, International Conference on Artificial Neural Networks, pages 609-612, Springer, London.