Topographic processing of relational data

WSOM 2007 - 6th Int. Workshop on Self-Organizing Maps

Volumn , Issue , 2007, Pages

  Hammer, Barbara ^a   Hasenfuss, Alexander ^a   Rossi, Fabrice ^b   Strickert, Marc ^c  

^a CLAUSTHAL UNIVERSITY OF TECHNOLOGY   (Germany)

^b INRIA   (France)

^c IPK Gatersleben   (Germany)

Author keywords

Batch clustering; NG; Relational data; SOM; Visualization

Indexed keywords

BATCH CLUSTERING; COMPLEX APPLICATIONS; DIRECT VISUALIZATION; EUCLIDEAN EMBEDDING; MULTI-DIMENSIONAL SCALING; NG; RELATIONAL DATA; SOM;

CONFORMAL MAPPING; DATA HANDLING; DATA VISUALIZATION; FLOW VISUALIZATION; OPTIMIZATION; VISUALIZATION;

MATRIX ALGEBRA;

EID: 84893449108     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (32)

1. James W. Anderson (2005), Hyperbolic Geometry, second edition, Springer.
2. B. Conan-Guez, F. Rossi, and A. El Golli (2006), Fast Algorithm and Implementation of Dissimilarity Self-Organizing Maps, Neural Networks 19:855-863.
3. M. Cottrell, B. Hammer, A. Hasenfuss, and T. Villmann (2006), Batch and median neural gas, Neural Networks 19:762-771.
4. T. Graepel, R. Herbrich, P. Bollmann-Sdorra and K. Obermayer (1999), Classification on pairwise proximity data, In M. I. Jordan, M. J. Kearns, and S. A. Solla (Eds.), NIPS, Vol. 11, MIT Press, p. 438-444.
5. T. Graepel and K. Obermayer (1999), A stochastic self-organizing map for proximity data, Neural Computation 11:139-155.
6. B. Haasdonk and C. Bahlmann (2004), Learning with distance substitution kernels, in Pattern Recognition - Proc. of the 26th DAGM Symposium.
7. B. Hammer, A. Hasenfuss (2007), Relational neural gas, to appear at KI 2007.
8. B. Hammer, A. Hasenfuss, F.-M. Schleif, and T. Villmann (2006), Supervised batch neural gas, in Proceedings of Conference Artificial Neural Networks in Pattern Recognition (ANNPR), F. Schwenker (ed.), Springer, pages 33-45.
9. R. J. Hathaway and J. C. Bezdek. Nerf c-means: Non-euclidean relational fuzzy clustering. Pattern Recognition 27(3):429-437, 1994.
10. R. J. Hathaway, J. W. Davenport, and J. C. Bezdek. Relational duals of the c-means algorithms. Pattern Recognition 22:205-212, 1989.
11. T. Heskes (2001). Self-organizing maps, vector quantization, and mixture modeling. IEEE Transactions on Neural Networks 12:1299-1305.
12. T. Hofmann and J. Buhmann Multidimensional Scaling and Data Clustering (1995), in Advances in Neural Information Processing Systems 7:459-466.
13. A. Juan and E. Vidal (2000), On the use of normalized edit distances and an efficient k-NN search technique (k-AESA) for fast and accurate string classification, in ICPR 2000, Vol. 2, p. 680-683.
14. S. Kaski, J. Nikkilä, E. Savia, and C. Roos (2005), Discriminative clustering of yeast stress response, in Bioinformatics using Computational Intelligence Paradigms, U. Seiffert, L. Jain, and P. Schweizer (eds.), pages 75-92, Springer.
15. S. Kaski, J. Nikkilä, M. Oja, J. Venna, P. Törönen, and E. Castren (2003), Trustworthiness and metrics in visualizing similarity of gene expression, BMC Bioinformatics, 4:48.
16. J. B. Kruskal (1964), Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychome-trika 29:1-27.
17. T. Kohonen (1982), Self-Organized formation of topologi-cally correct feature maps, Biological Cybernetics, 43:59-69.
18. T. Kohonen and P. Somervuo (2002), How to make large self-organizing maps for nonvectorial data, Neural Networks 15:945-952.
19. C. Lundsteen, J. Phillip, and E. Granum (1980), Quantitative analysis of 6985 digitized trypsin G-banded human metaphase chromosomes, Clinical Genetics 18:355-370.
20. T. Martinetz, S.G. Berkovich, and K.J. Schulten (1993), 'Neural-gas' network for vector quantization and its application to time-series prediction, IEEE Transactions on Neural Networks 4:558-569.
21. H. Mevissen and M. Vingron (1996), Quantifying the local reliability of a sequence alignment, Protein Engineering 9:127-132.
22. M. Neuhaus and H. Bunke (2006), Edit distance based kernel functions for structural pattern classification Pattern Recognition 39(10):1852-1863.
23. A.K. Qin and P.N. Suganthan (2004), Kernel neural gas algorithms with application to cluster analysis, ICPR 2004 Vol. 4, pp. 617-620.
24. H. Ritter (1999), Self-organizing Maps in non-euclidean Spaces, Kohonen Maps, 97-108, Eds.: E. Oja and S. Kaski.
25. F. Rossi, A. Hasenfuss, B. Hammer, Accelerating relational clustering algorithms with sparse prototype representation, submitted to WSOM'07.
26. A. Saalbach, T. Twellmann, A. Wismüller, J. Ontrup, H. Ritter, T. W. Nattkemper (2005), A Hyperbolic Topographic Mapping for Proximity Data, Proc. of the IASTED International Conference on Artifical Intelligence and Applications, 106-111, ACTA Press.
27. J. W. Sammon Jr. (1969), A nonlinear mapping for data structure analysis. IEEE Transactions on Computers 18:401-409.
28. S. Seo and K. Obermayer (2004), Self-organizing maps and clustering methods for matrix data, Neural Networks 17:1211-1230.
29. M. Strickert, U. Seiffert, N. Sreenivasulu, W. Weschke, T. Villmann, B. Hammer (2006), Generalized Relevance LVQ (GRLVQ) with Correlation Measures for Gene Expression Analysis, Neurocomputing 69: 651-659.
30. P. Tino, A. Kaban, and Y. Sun (2004), A generative probabilistic approach to visualizing sets of symbolic sequences. In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining-KDD-2004, (eds) R. Kohavi, J. Gehrke, W. DuMouchel, J. Ghosh. pp. 701-706, ACM Press.
31. J. Walter (2004), H-MDS: a new approach for interactive visualization with multidimensional scaling in the hyperbolic space. Information Systems, 29(4):273-292.
32. H. Yin (2006), On the equivalence between kernel self-organising maps and self-organising mixture density network, Neural Networks 19(6):780-784.