Texto completo
Vista Previa |
PDF (Portable Document Format)
- Se necesita un visor de ficheros PDF, como GSview, Xpdf o Adobe Acrobat Reader
Descargar (879kB) | Vista Previa |
Growing Self-Organizing Maps for Data Analysis
Cita
Delgado Sanz, Maria Soledad 
ORCID: https://orcid.org/0000-0003-4868-3712, Gonzalo Martín, Consuelo ORCID: https://orcid.org/0000-0002-0804-9293, Martínez Izquierdo, María Estíbaliz ORCID: https://orcid.org/0000-0003-0296-6151 and Arquero Hidalgo, Águeda ORCID: https://orcid.org/0000-0002-3590-1162
(2009).
Growing Self-Organizing Maps for Data Analysis.
En:
"Encyclopedia of Artificial Intelligence".
IGI Publications, EEUU, pp. 781-787.
ISBN 978-1-59904-849-9.
https://doi.org/10.4018/978-1-59904-849-9.
ORCID: https://orcid.org/0000-0003-4868-3712, Gonzalo Martín, Consuelo ORCID: https://orcid.org/0000-0002-0804-9293, Martínez Izquierdo, María Estíbaliz ORCID: https://orcid.org/0000-0003-0296-6151 and Arquero Hidalgo, Águeda ORCID: https://orcid.org/0000-0002-3590-1162
(2009).
Growing Self-Organizing Maps for Data Analysis.
En:
"Encyclopedia of Artificial Intelligence".
IGI Publications, EEUU, pp. 781-787.
ISBN 978-1-59904-849-9.
https://doi.org/10.4018/978-1-59904-849-9.
Descripción
| Título: | Growing Self-Organizing Maps for Data Analysis |
|---|---|
| Autor/es: |
|
| Tipo de Documento: | Sección de Libro |
| Título del Libro: | Encyclopedia of Artificial Intelligence |
| Fecha: | 2009 |
| ISBN: | 978-1-59904-849-9 |
| Volumen: | 3 |
| Materias: | |
| ODS: | |
| Palabras Clave Informales: | Artificial Neural Networks, Data Mining,Exploratory Data Analysis, Growing Cell Structures, Knowledge Visualization, Self-Organizing Map, Unsupervised Learning. |
| Escuela: | Facultad de Informática (UPM) [antigua denominación] |
| Departamento: | Arquitectura y Tecnología de Sistemas Informáticos |
| Licencias Creative Commons: | Reconocimiento - Sin obra derivada - No comercial |
Resumen
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.
Más información
| ID de Registro: | 4951 |
|---|---|
| Identificador DC: | https://oa.upm.es/4951/ |
| Identificador OAI: | oai:oa.upm.es:4951 |
| Identificador DOI: | 10.4018/978-1-59904-849-9 |
| URL Oficial: | http://www.igi-global.com/Bookstore/TitleDetails.a... |
| Depositado por: | Memoria Investigacion |
| Depositado el: | 18 Nov 2010 11:25 |
| Ultima Modificación: | 19 Feb 2025 10:58 |
Acciones
- Estadísticas
- Exportar cita
- Editar (sólo personal del Archivo)
