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Growing Self-Organizing Maps for Data Analysis
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Author(s): Soledad Delgado (Technical University of Madrid, Spain), Consuelo Gonzalo (Technical University of Madrid, Spain), Estíbaliz Martínez (Technical University of Madrid, Spain)and Águeda Arquero (Technical University of Madrid, Spain)
Copyright: 2009
Pages: 7
Source title:
Encyclopedia of Artificial Intelligence
Source Author(s)/Editor(s): Juan Ramón Rabuñal Dopico (University of A Coruña, Spain), Julian Dorado (University of A Coruña, Spain)and Alejandro Pazos (University of A Coruña, Spain)
DOI: 10.4018/978-1-59904-849-9.ch116
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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.
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