Growing Self-Organizing Maps for Data Analysis

Delgado Sanz, Maria Soledad ORCID:, Gonzalo Martín, Consuelo ORCID:, Martínez Izquierdo, María Estíbaliz ORCID: and Arquero Hidalgo, Águeda ORCID: (2009). Growing Self-Organizing Maps for Data Analysis. In: "Encyclopedia of Artificial Intelligence". IGI Publications, EEUU, pp. 781-787. ISBN 978-1-59904-849-9.


Title: Growing Self-Organizing Maps for Data Analysis
Item Type: Book Section
Title of Book: Encyclopedia of Artificial Intelligence
Date: 2009
ISBN: 978-1-59904-849-9
Volume: 3
Freetext Keywords: Artificial Neural Networks, Data Mining,Exploratory Data Analysis, Growing Cell Structures, Knowledge Visualization, Self-Organizing Map, Unsupervised Learning.
Faculty: Facultad de Informática (UPM)
Department: Arquitectura y Tecnología de Sistemas Informáticos
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Currently, there exist many research areas that produce large multivariable datasets that are difficult to visualize in order to extract useful information. Kohonen self organizing 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 display 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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Item ID: 4951
DC Identifier:
OAI Identifier:
DOI: 10.4018/978-1-59904-849-9
Official URL:
Deposited by: Memoria Investigacion
Deposited on: 18 Nov 2010 11:25
Last Modified: 20 Apr 2016 13:57
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