Abstract
The Self-Organizing Map (SOM) is a popular algorithm to analyze the structure of a dataset. However, some topological constraints of the SOM are fixed before the learning and may not be relevant regarding to the data structure. In this paper we propose to improve the SOM performance with a new algorithm which learn the topological constraints of the map using data structure information. Experiments on artificial and real databases show that algorithm achieve better results than SOM. This is not the case with trivial topological constraint relaxation because of the high increase of the Topological error.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kohonen, T.: Self-Organizing Maps. Springer, Berlin (2001)
Fritzke, B.: Growing grid - a self-organizing network with constant neighborhood range and adaptation strength. Neural Processing Letters 2(5), 9–13 (1995)
Cabanes, G., Bennani, Y.: A local density-based simultaneous two-level algorithm for topographic clustering. In: Proceeding of the International Joint Conference on Neural Networks, pp. 1176–1182 (2008)
Matsushita, H., Nishio, Y.: Self-Organizing Map with False-Neighbor Degree between Neurons for Effective Self-Organization. IEICE Transactions on Fundamentals E91-A(6), 1463–1469 (2008)
Kohonen, T.: Self-Organization and Associative Memory. Springer, Berlin (1984)
Johnson, D.: Efficient algorithms for shortest paths in sparse networks. Journal of the ACM 24(1), 1–13 (1977)
Ultsch, A.: Clustering with SOM: U*C. In: Procedings of the Workshop on Self-Organizing Maps, Paris, France, pp. 75–82 (2005)
Frank, A., Asuncion, A.: UCI machine learning repository (2010)
Kiviluoto, K.: Topology Preservation in Self-Organizing Maps. In: International Conference on Neural Networks, pp. 294–299 (1996)
Cheung, Y., Law, L.: Rival-Model Penalized Self-Organizing Map. IEEE Trans. Neural Networks 18(1), 289–295 (2007)
Vesanto, J., Himberg, J., Alhoniemi, E., Parhankangas, J.: Self-Organizing Map in Matlab: the SOM Toolbox. In: Proceedings of the Matlab DSP Conference, pp. 35–40 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cabanes, G., Bennani, Y. (2010). Learning Topological Constraints in Self-Organizing Map. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Models and Applications. ICONIP 2010. Lecture Notes in Computer Science, vol 6444. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17534-3_45
Download citation
DOI: https://doi.org/10.1007/978-3-642-17534-3_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17533-6
Online ISBN: 978-3-642-17534-3
eBook Packages: Computer ScienceComputer Science (R0)