Fast algorithm and implementation of dissimilarity self-organizing maps

Neural Networks

Volumn 19, Issue 6-7, 2006, Pages 855-863

  Conan Guez, Brieuc ^a   Rossi, Fabrice ^b   El Golli, Aïcha ^b  

^a UNIVERSITÉ DE LORRAINE   (France)

^b INRIA   (France)

Author keywords

Clustering; Dissimilarity data; Fast implementation; Nonlinear projection; Pairwise Data; Proximity Data; Self organizing map; Unsupervised learning

Indexed keywords

ALGORITHMS; COSTS; DATA REDUCTION; DATABASE SYSTEMS; MATRIX ALGEBRA; NONLINEAR EQUATIONS; VECTORS;

CLUSTERING; DISSIMILARITY DATA; FAST IMPLEMENTATION; PAIRWISE DATA; PROXIMITY DATA; SELF-ORGANIZING MAP; UNSUPERVISED LEARNING;

SELF ORGANIZING MAPS;

ACCURACY; ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; CLUSTER ANALYSIS; DATA ANALYSIS; LINEAR SYSTEM; MATHEMATICAL ANALYSIS; PRIORITY JOURNAL; SELF ORGANIZING MAP; SIMULATION;

ALGORITHMS; HUMANS; INFORMATION SERVICES; LIKELIHOOD FUNCTIONS; NEURAL NETWORKS (COMPUTER); NONLINEAR DYNAMICS; PATTERN RECOGNITION, AUTOMATED;

EID: 33746893766     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neunet.2006.05.002     Document Type: Article

References (24)

1. Ambroise, C., & Govaert, G. (1996) Analyzing dissimilarity matrices via Kohonen maps. In Proceedings of 5th conference of the international federation of classification societies. Vol. 2 (pp. 96-99)
2. Atkinson K. Spell checking oriented word lists (SCOWL) (2004). http://wordlist.sourceforge.net/ revision 6
3. Bahlmann C., and Burkhardt H. The writer independent online handwriting recognition system frog on hand and cluster generative statistical dynamic time warping. IEEE Transactions on Pattern Analysis and Machine Intelligence 26 3 (2004) 299-310
4. Buhmann J.M., and Hofmann T. A maximum entropy approach to pairwise data clustering. Proceedings of the international conference on pattern recognition Vol. II (1994), IEEE Computer Society Press, Hebrew University, Jerusalem, Israel 207-212
5. El Golli A., Conan-Guez B., and Rossi F. Self organizing map and symbolic data. Journal of Symbolic Data Analysis 2 1 (2004)
6. El Golli A., Conan-Guez B., and Rossi F. A self organizing map for dissimilarity data. In: Banks D., House L., McMorris F.R., Arabie P., and Gaul W. (Eds). Classification, clustering, and data mining applications (Proceedings of IFCS 2004) (2004), IFCS, Springer, Chicago, Illinois, USA 61-68
7. Graepel T., Burger M., and Obermayer K. Self-organizing maps: Generalizations and new optimization techniques. Neurocomputing 21 (1998) 173-190
8. Graepel T., and Obermayer K. A stochastic self-organizing map for proximity data. Neural Computation 11 1 (1999) 139-155
9. Hammer B., Micheli A., Strickert M., and Sperduti A. A general framework for unsupervised processing of structured data. Neurocomputing 57 (2004) 3-35
10. Hammer, B., & Jain B. J. (2004). Neural methods for non-standard data. In Proceedings of ESANN 2004 (pp. 281-292)
11. Hammer, B., & Villmann, T. (2005). Classification using non standard metrics. In Proceedings of ESANN 2005 (pp. 303-316)
12. Hofmann T., and Buhmann J.M. Hierarchical pairwise data clustering by mean-field annealing. Proceedings of the international conference on artificial neural networks (1995), Springer 197-202
13. Hofmann T., and Buhmann J.M. Pairwise data clustering by deterministic annealing. IEEE Transactions on Pattern Analysis and Machine Intelligence 19 1 (1997) 1-14
14. Kaufman L., and Rousseeuw P.J. Clustering by means of medoids. In: Dodge Y. (Ed). Statistical data analysis based on the L1-norm and related methods (1987), North-Holland 405-416
15. Kohonen T. Self-organizing maps. 3rd ed. Springer series in information sciences Vol. 30 (1995), Springer last edition published in 2001
16. Kohonen T., Kaski S., Lagus K., Salöjarvi J., Honkela J., Paatero V., and Saarela A. Self organization of a massive text document collection. IEEE Transactions on Neural Networks 11 3 (2000) 574-585
17. Kohonen T., and Somervuo P.J. Self-organizing maps of symbol strings. Neurocomputing 21 (1998) 19-30
18. Kohonen T., and Somervuo P.J. How to make large self-organizing maps for nonvectorial data. Neural Networks 15 8 (2002) 945-952
19. Levenshtein V.I. Binary codes capable of correcting deletions, insertions and reversals. Soviet Physics-Doklady 10 8 (1966) 707-710
20. MacQueen J.B. Some methods for classification and analysis of multivariate observations. Proceedings of 5-th Berkeley symposium on mathematical statistics and probability (1967), University of California Press, Berkeley, USA 281-297
21. Porter M.F. An algorithm for suffix stripping. Program 14 3 (1980) 130-137
22. Rossi, F., El Golli, A., & Lechevallier, Y. (2005) Usage guided clustering of web pages with the median self organizing map. In Proceedings of XIIIth European symposium on artificial neural networks (pp. 351-356)
23. Seo S., and Obermayer K. Self-organizing maps and clustering methods for matrix data. Neural Networks 17 8-9 (2004) 1211-1229
24. Wei C.-P., Lee Y.-H., and Hsu C.-M. Empirical comparison of fast partitioning-based clustering algorithms for large data sets. Expert Systems with Application 24 4 (2003) 353-363