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SOM based visualization in data analysis

  • Part IV:Signal Processing: Blind Source Separation, Vector Quantization, and Self Organization
  • Conference paper
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Artificial Neural Networks — ICANN'97 (ICANN 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1327))

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Abstract

Visualization is an important part of data analysis, especially when exploring multidimensional data. Our approach uses the selforganizing map (SOM) as a basic method because it provides a good basis for data visualization. The developed analysis tool utilizes the structure of SOM and is integrated with a generally applicable visualization system. In addition, we propose a model to link several SOM presentations for visualizing more complex structures of information.

This work was supported by TEKES under Stella project.

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Authors

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Wulfram Gerstner Alain Germond Martin Hasler Jean-Daniel Nicoud

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© 1997 Springer-Verlag Berlin Heidelberg

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Häkkinen, E., Koikkalainen, P. (1997). SOM based visualization in data analysis. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020220

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  • DOI: https://doi.org/10.1007/BFb0020220

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63631-1

  • Online ISBN: 978-3-540-69620-9

  • eBook Packages: Springer Book Archive

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