Observatorio de I+D+i UPM

Descripción
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.
Internacional	No
DOI
Edición del Libro	1
Editorial del Libro	IGI Publications
ISBN	978-1-59904-849-9
Serie
Título del Libro	Encyclopedia of Artificial Intelligence
Desde página	781
Hasta página	787

Esta actividad pertenece a memorias de investigación

• Autor: Maria Soledad Delgado Sanz (UPM)
• Autor: M.Estibaliz Martinez Izquierdo (UPM)
• Autor: Agueda Arquero Hidalgo (UPM)
• Autor: Consuelo Gonzalo Martin (UPM)

• Creador: Departamento: Organización y Estructura de la Información
• Departamento: Arquitectura y Tecnología de Sistemas Informáticos