Abstract
When used for visualization of high dimensional data, the self-organizing maps (SOM) requires a coloring scheme, or interpolation, or applying some projection techniques to analyze the intrinsic structure of the data. Even so, the structures of the data clusters may not be apparent and their shapes are often distorted. In order to overcome some shortcomings of the traditional SOM visualization method a novel technique is presented in this paper. Several experimental data sets including the chain-link problem and IRIS data are used to test the approach. The analysis results prove that the presented technique provides a better picture of the high dimensional data to understand their intrinsic structure.
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© 2005 Springer-Verlag Berlin Heidelberg
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Liao, G., Shi, T., Liu, S., Xuan, J. (2005). A Novel Technique for Data Visualization Based on SOM. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Biological Inspirations – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3696. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550822_66
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DOI: https://doi.org/10.1007/11550822_66
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28752-0
Online ISBN: 978-3-540-28754-4
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