Knowledge discovery process for description of spatially referenced clusters

Autores
Róttoli, Giovanni; Merlino, Hernán; García Martínez, Ramón
Año de publicación
2017
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Spatial clustering is an important field of spatial data mining and knowledge discovery that serves to partition a spatial data set to obtain disjoint subsets with spatial elements that are similar to each other. Existing algorithms can be used to perform three types of cluster analyses, including clustering of spatial points, regionalization and point pattern analysis. However, all these existing methods do not provide a description of the discovered spatial clusters, which is useful for decision making in many different fields. This work proposes a knowledge discovery process for the description of spatially referenced clusters that uses decision tree learning algorithms. Two proofs of concept of the proposed process using different spatial clustering algorithm on real data are also provided.
Facultad de Informática
Materia
Ciencias Informáticas
Decision tree learning
Knowledge discovery process
Regionalization
Spatial clustering
Spatial data mining
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/87556
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/87556
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Knowledge discovery process for description of spatially referenced clustersRóttoli, GiovanniMerlino, HernánGarcía Martínez, RamónCiencias InformáticasDecision tree learningKnowledge discovery processRegionalizationSpatial clusteringSpatial data miningSpatial clustering is an important field of spatial data mining and knowledge discovery that serves to partition a spatial data set to obtain disjoint subsets with spatial elements that are similar to each other. Existing algorithms can be used to perform three types of cluster analyses, including clustering of spatial points, regionalization and point pattern analysis. However, all these existing methods do not provide a description of the discovered spatial clusters, which is useful for decision making in many different fields. This work proposes a knowledge discovery process for the description of spatially referenced clusters that uses decision tree learning algorithms. Two proofs of concept of the proposed process using different spatial clustering algorithm on real data are also provided.Facultad de Informática2017-07info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf410-415http://sedici.unlp.edu.ar/handle/10915/87556enginfo:eu-repo/semantics/altIdentifier/isbn/1891706411info:eu-repo/semantics/altIdentifier/issn/2325-9000info:eu-repo/semantics/altIdentifier/doi/10.18293/SEKE2017-013info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T10:49:35Zoai:sedici.unlp.edu.ar:10915/87556Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:49:36.202SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Knowledge discovery process for description of spatially referenced clusters
title Knowledge discovery process for description of spatially referenced clusters
spellingShingle Knowledge discovery process for description of spatially referenced clusters
Róttoli, Giovanni
Ciencias Informáticas
Decision tree learning
Knowledge discovery process
Regionalization
Spatial clustering
Spatial data mining
title_short Knowledge discovery process for description of spatially referenced clusters
title_full Knowledge discovery process for description of spatially referenced clusters
title_fullStr Knowledge discovery process for description of spatially referenced clusters
title_full_unstemmed Knowledge discovery process for description of spatially referenced clusters
title_sort Knowledge discovery process for description of spatially referenced clusters
dc.creator.none.fl_str_mv Róttoli, Giovanni
Merlino, Hernán
García Martínez, Ramón
author Róttoli, Giovanni
author_facet Róttoli, Giovanni
Merlino, Hernán
García Martínez, Ramón
author_role author
author2 Merlino, Hernán
García Martínez, Ramón
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Decision tree learning
Knowledge discovery process
Regionalization
Spatial clustering
Spatial data mining
topic Ciencias Informáticas
Decision tree learning
Knowledge discovery process
Regionalization
Spatial clustering
Spatial data mining
dc.description.none.fl_txt_mv Spatial clustering is an important field of spatial data mining and knowledge discovery that serves to partition a spatial data set to obtain disjoint subsets with spatial elements that are similar to each other. Existing algorithms can be used to perform three types of cluster analyses, including clustering of spatial points, regionalization and point pattern analysis. However, all these existing methods do not provide a description of the discovered spatial clusters, which is useful for decision making in many different fields. This work proposes a knowledge discovery process for the description of spatially referenced clusters that uses decision tree learning algorithms. Two proofs of concept of the proposed process using different spatial clustering algorithm on real data are also provided.
Facultad de Informática
description Spatial clustering is an important field of spatial data mining and knowledge discovery that serves to partition a spatial data set to obtain disjoint subsets with spatial elements that are similar to each other. Existing algorithms can be used to perform three types of cluster analyses, including clustering of spatial points, regionalization and point pattern analysis. However, all these existing methods do not provide a description of the discovered spatial clusters, which is useful for decision making in many different fields. This work proposes a knowledge discovery process for the description of spatially referenced clusters that uses decision tree learning algorithms. Two proofs of concept of the proposed process using different spatial clustering algorithm on real data are also provided.
publishDate 2017
dc.date.none.fl_str_mv 2017-07
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/87556
url http://sedici.unlp.edu.ar/handle/10915/87556
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/1891706411
info:eu-repo/semantics/altIdentifier/issn/2325-9000
info:eu-repo/semantics/altIdentifier/doi/10.18293/SEKE2017-013
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
410-415
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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