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)


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.


Self-organizing map topologies Visualization Spanning trees Unsupervised learning 


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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

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