Visualization of clusters in geo-referenced data using three-dimensional self-organizing maps

Detalhes bibliográficos
Autor(a) principal: Gorricha, Jorge Manuel Lourenço
Data de Publicação: 2010
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/2631
Resumo: Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação
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spelling Visualization of clusters in geo-referenced data using three-dimensional self-organizing mapsSelf-organizing mapClusters analysisGeo-referenced data3D SOMVisualizationFrontiersUnsupervised neural networksClusteringDissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de InformaçãoThe Self-Organizing Map (SOM) is an artificial neural network that performs simultaneously vector quantization and vector projection. Due to this characteristic, the SOM is an effective method for clustering analysis via visualization. The SOM can be visualized through the output space, generally a regular two-dimensional grid of nodes, and through the input space, emphasizing the vector quantization process. Among all the strategies for visualizing the SOM, we are particularly interested in those that allow dealing with spatial dependency, linking the SOM to the geographic visualization with color. One possible approach, commonly used, is the cartographic representation of data with label colors defined from the output space of a two-dimensional SOM. However, in the particular case of geo-referenced data, it is possible to consider the use of a three-dimensional SOM for this purpose, thus adding one more dimension in the analysis. In this dissertation is presented a method for clustering geo-referenced data that integrates the visualization of both perspectives of a three dimensional SOM: linking its output space to the cartographic representation through a ordered set of colors; and exploring the use of frontiers among geo-referenced elements, computed according to the distances in the input space between their Best Matching Units.Lobo, Victor José de Almeida e SousaRUNGorricha, Jorge Manuel Lourenço2010-02-11T20:06:34Z2010-01-192010-01-19T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/2631enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-10T15:23:50ZPortal AgregadorONG
dc.title.none.fl_str_mv Visualization of clusters in geo-referenced data using three-dimensional self-organizing maps
title Visualization of clusters in geo-referenced data using three-dimensional self-organizing maps
spellingShingle Visualization of clusters in geo-referenced data using three-dimensional self-organizing maps
Gorricha, Jorge Manuel Lourenço
Self-organizing map
Clusters analysis
Geo-referenced data
3D SOM
Visualization
Frontiers
Unsupervised neural networks
Clustering
title_short Visualization of clusters in geo-referenced data using three-dimensional self-organizing maps
title_full Visualization of clusters in geo-referenced data using three-dimensional self-organizing maps
title_fullStr Visualization of clusters in geo-referenced data using three-dimensional self-organizing maps
title_full_unstemmed Visualization of clusters in geo-referenced data using three-dimensional self-organizing maps
title_sort Visualization of clusters in geo-referenced data using three-dimensional self-organizing maps
author Gorricha, Jorge Manuel Lourenço
author_facet Gorricha, Jorge Manuel Lourenço
author_role author
dc.contributor.none.fl_str_mv Lobo, Victor José de Almeida e Sousa
RUN
dc.contributor.author.fl_str_mv Gorricha, Jorge Manuel Lourenço
dc.subject.por.fl_str_mv Self-organizing map
Clusters analysis
Geo-referenced data
3D SOM
Visualization
Frontiers
Unsupervised neural networks
Clustering
topic Self-organizing map
Clusters analysis
Geo-referenced data
3D SOM
Visualization
Frontiers
Unsupervised neural networks
Clustering
description Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação
publishDate 2010
dc.date.none.fl_str_mv 2010-02-11T20:06:34Z
2010-01-19
2010-01-19T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/2631
url http://hdl.handle.net/10362/2631
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv
repository.mail.fl_str_mv
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