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Visualization of Complex Datasets with the Self-Organizing Spanning Tree

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Advances in Computational Intelligence (IWANN 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9094))

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Abstract

Visualization of real world data is a difficult task due to the high-dimensional and the complex structure in real datasets. Scientific data visualization requires a variety of mathematical techniques to transform high-dimensional data sets into simple graphical objects that provide a clearer understanding. In this work a Self-Organizing Spanning Tree is proposed, which is able to learn a tree topology without any prespecified structure. Experimental results are provided to show the good performance with synthetic and real data. Moreover, the proposed self-organizing model is applied to color vector quantization, whose comparative results are provided.

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Authors and Affiliations

  1. Department of Computer Languages and Computer Science, University of Málaga, Bulevar Louis Pasteur, 35, 29071, Málaga, Spain

    Ezequiel López-Rubio, Esteban José Palomo & Enrique Domínguez

  2. Department of Computer Systems and Telematics Engineering, University of Extremadura, University Centre of Mérida, 06800, Mérida, Spain

    Rafael Marcos Luque Baena

Authors
  1. Ezequiel López-Rubio
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  2. Esteban José Palomo
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  3. Rafael Marcos Luque Baena
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  4. Enrique Domínguez
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Corresponding author

Correspondence to Ezequiel López-Rubio .

Editor information

Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Malaga, Malaga, Spain

    Gonzalo Joya

  3. Polytechnic University of Catalonia, Vilanova i la Geltrú, Spain

    Andreu Catala

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© 2015 Springer International Publishing Switzerland

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López-Rubio, E., Palomo, E.J., Luque Baena, R.M., Domínguez, E. (2015). Visualization of Complex Datasets with the Self-Organizing Spanning Tree. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2015. Lecture Notes in Computer Science(), vol 9094. Springer, Cham. https://doi.org/10.1007/978-3-319-19258-1_18

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  • DOI: https://doi.org/10.1007/978-3-319-19258-1_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19257-4

  • Online ISBN: 978-3-319-19258-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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