Observatorio de I+D+i UPM

Memorias de investigación
Book chapters:
Growing Self-Organizing Maps for Data Analysis
Year:2008
Research Areas
  • Artificial intelligence
Information
Abstract
Currently, there exist many research areas that produce large multivariable datasets that are difficult to visualize in order to extract useful information. Kohonen selforganizing maps have been used successfully in the visualization and analysis of multidimensional data. In this work, a projection technique that compresses multidimensional datasets into two dimensional space using growing self-organizing maps is described. With this embedding scheme, traditional Kohonen visualization methods have been implemented using growing cell structures networks. New graphical map displays have been compared with Kohonen graphs using two groups of simulated data and one group of real multidimensional data selected from a satellite scene.
International
No
Book Edition
1
Book Publishing
IGI Publications
ISBN
978-1-59904-849-9
Series
Book title
Encyclopedia of Artificial Intelligence
From page
781
To page
787
Participants
  • Autor: Maria Soledad Delgado Sanz (UPM)
  • Autor: M.Estibaliz Martinez Izquierdo (UPM)
  • Autor: Agueda Arquero Hidalgo (UPM)
  • Autor: Consuelo Gonzalo Martin (UPM)
Research Group, Departaments and Institutes related
  • Creador: Departamento: Organización y Estructura de la Información
  • Departamento: Arquitectura y Tecnología de Sistemas Informáticos
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