Growing hierarchical principal components analysis self-organizing map

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3971 LNCS, Issue , 2006, Pages 701-706

  Zhang, Stones Lei ^a   Yi, Zhang ^a   Lv, Jian Cheng ^a  

^a UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER ARCHITECTURE; MATHEMATICAL MODELS; NEURAL NETWORKS; PROBLEM SOLVING;

HIERARCHICAL STRUCTURE; NEURAL NETWORKS MODEL; TRADITION CLUSTERING PROBLEM;

PRINCIPAL COMPONENT ANALYSIS;

EID: 33745899587     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11759966_103     Document Type: Conference Paper

References (10)

1. Lopez-Rubio, E., Munoz-Perez, J.Gomez-Ruiz J.A: A Principal Components Analysis Self-Organizing Map. Neural Networks 17(2004) 261-270
2. Kohonen, T.: Emergence of Invariant-Feature Dectors in the Adaptive-Subspace SOM. Biological Cybernetics 75(1996) 281-291
3. Kohonen, T.: The Self-Organizing Map. Proc. IEEE 78(1990) 1464-1480
4. Blackmore, J. and Miikkulainen, R.: Incremental Grid Growing: Encoding High-Dimensional Structure into a Two-Dimensional Feature Map. in Proc. IEEE Int. Conf. Neural Networks, San Francisco, CA 1(1993) 450-455
5. Fritzke, B.: Growing GridA Self-Organizing Network with Constant Neighborhood Range and Adaption Strength. Neural Processing Letter 2(1995) 1-5
6. Alahakoon, D., Halgamuge, S. K., and Srinivasan, B.: Dynamic Self-Organizing Maps with Controlled Growth for Knowledge Discovery. IEEE Trans. Neural Networks 11(2000) 601-614.
7. Rauber, A., Merkl, D., and Dittenbach, M.: The Growing Hierarchical Self-organizing Map: Exploratory Analysis of High-Dimensional Data. IEEE Trans. on Neural Networks 13(6)(2002) 1331-1341
8. Moreno, S., Allende, H., Rogel, C. and Salas, R.: Robust Growing Hierarchical Self-Organizing Map. In: J. Cabestany, A. Pricto, and D.F. Sandoval(Eds.): IWANN2005, LNCS, Vol. 3512 Springer-Verlag, Berlin Heidelberg (2005)341-348
9. Pampalk, E., Widmer, G. and Chan, A.: A New Approach to Hierarchical Clustering and Structuring of Data with Self-Oranizing Maps. Intell. Data Analysis 8(2004) 131-149
10. Murphy,P.M.:UCI Repository of Machine Learning Database and Domain Theories[online]. Available: http://www.ics.uci.edu/mlearn/MLRepository.html. Data of access: March 2001