Advertisement

Visualization of Complex Datasets with the Self-Organizing Spanning Tree

  • Ezequiel López-RubioEmail author
  • Esteban José Palomo
  • Rafael Marcos Luque Baena
  • Enrique Domínguez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9094)

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.

Keywords

Self-organizing map topologies Visualization Spanning trees Unsupervised learning 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kohonen, T.: Self-Organizing Maps, 3nd edn. Springer (2001)Google Scholar
  2. 2.
    Kohonen, T.: Essentials of the self-organizing map. Neural Networks 37, 52–65 (2013)CrossRefGoogle Scholar
  3. 3.
    Yin, H.: The self-organizing maps: Background, theories, extensions and applications. Studies in Computational Intelligence 115, 715–762 (2008)Google Scholar
  4. 4.
    Astudillo, C., Oommen, B.: Topology-oriented self-organizing maps: A survey. Pattern Analysis and Applications 17(2), 223–248 (2014)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Pakkanen, J., Iivarinen, J., Oja, E.: The evolving tree - analysis and applications. IEEE Transactions on Neural Networks 17(3), 591–603 (2006)CrossRefGoogle Scholar
  6. 6.
    Luo, F., Khan, L., Bastani, F., Yen, I.L., Zhou, J.: A dynamically growing self-organizing tree (dgsot) for hierarchical clustering gene expression profiles. Bioinformatics 20(16), 2605–2617 (2004)CrossRefGoogle Scholar
  7. 7.
    Doan, N.Q., Azzag, H., Lebbah, M.: Growing self-organizing trees for autonomous hierarchical clustering. Neural Networks 41, 85–95 (2013)zbMATHCrossRefGoogle Scholar
  8. 8.
    Xu, P., Chang, C.H., Paplinski, A.: Self-organizing topological tree for online vector quantization and data clustering. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 35(3), 515–526 (2005)CrossRefGoogle Scholar
  9. 9.
    Samsonova, E., Kok, J., IJzerman, A.: Treesom: Cluster analysis in the self-organizing map. Neural Networks 19(6–7), 935–949 (2006)zbMATHCrossRefGoogle Scholar
  10. 10.
    Astudillo, C., John Oommen, B.: Imposing tree-based topologies onto self organizing maps. Information Sciences 181(18), 3798–3815 (2011)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing 13(4), 600–612 (2004)CrossRefGoogle Scholar
  12. 12.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: The SSIM Index for Image Quality Assessment (2003). https://ece.uwaterloo.ca/ z70wang/research/ssim/ (accessed January 31, 2015)

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Ezequiel López-Rubio
    • 1
    Email author
  • Esteban José Palomo
    • 1
  • Rafael Marcos Luque Baena
    • 2
  • Enrique Domínguez
    • 1
  1. 1.Department of Computer Languages and Computer ScienceUniversity of MálagaMálagaSpain
  2. 2.Department of Computer Systems and Telematics EngineeringUniversity of Extremadura, University Centre of MéridaMéridaSpain

Personalised recommendations