Knowledge discovery process for description of spatially referenced clusters

Autores
Rottoli, Giovanni Daián; Merlino, Hernán Daniel; 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 spat ial clustering algorithm on real data are also provided.
Fil: Rottoli, Giovanni Daián. Universidad Nacional de Lanús; Argentina.
Fil: Merlino, Hernán Daniel. Universidad Nacional de Lanús; Argentina.
Fil: García Martínez, Ramón. Universidad Nacional de Lanús. Departamento Desarrollo Productivo y Tecnológico. Grupo de Investigación en Sistemas de Información; Argentina.
Fil: Rottoli, Giovanni Daián. Universidad Tecnológica Nacional. Facultad Regional Concepción del Uruguay. Departamento Ingeniería en Sistemas de Información. Grupo de Investigación en Bases de Datos; Argentina.
Fil: Rottoli, Giovanni Daián. Universidad Nacional de La Plata; Argentina.
Fil: García Martínez, Ramón. Comisión de Investigaciones Científicas; Argentina.
Materia
Knowledge discovery process
Spatial clustering
Regionalization
Decision tree learning
Spatial data mining
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-nd/4.0/
Repositorio
Repositorio Institucional Abierto (UTN)
Institución
Universidad Tecnológica Nacional
OAI Identificador
oai:ria.utn.edu.ar:20.500.12272/3323
Acceder
id RIAUTN_eed1cb1c8e67cbbc3ab1a3319848fd62
oai_identifier_str oai:ria.utn.edu.ar:20.500.12272/3323
network_acronym_str RIAUTN
repository_id_str a
network_name_str Repositorio Institucional Abierto (UTN)
spelling Knowledge discovery process for description of spatially referenced clustersRottoli, Giovanni DaiánMerlino, Hernán DanielGarcía Martínez, RamónKnowledge discovery processSpatial clusteringRegionalizationDecision tree learningSpatial 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 spat ial clustering algorithm on real data are also provided.Fil: Rottoli, Giovanni Daián. Universidad Nacional de Lanús; Argentina.Fil: Merlino, Hernán Daniel. Universidad Nacional de Lanús; Argentina.Fil: García Martínez, Ramón. Universidad Nacional de Lanús. Departamento Desarrollo Productivo y Tecnológico. Grupo de Investigación en Sistemas de Información; Argentina.Fil: Rottoli, Giovanni Daián. Universidad Tecnológica Nacional. Facultad Regional Concepción del Uruguay. Departamento Ingeniería en Sistemas de Información. Grupo de Investigación en Bases de Datos; Argentina.Fil: Rottoli, Giovanni Daián. Universidad Nacional de La Plata; Argentina.Fil: García Martínez, Ramón. Comisión de Investigaciones Científicas; Argentina.2018-12-13T11:47:04Z2018-12-13T11:47:04Z2017info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfapplication/pdfInternational Conference on Software Engineering & Knowledge Engineering. Ed. USA KSI Research Inc. and Knowledge Systems Institute, 410415 (2017)http://hdl.handle.net/20.500.12272/332310.18293/SEKE2017-013engenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/Rottoli, Giovanni Daián ; Merlino, Hernán Daniel ; García Martínez, RamónNo comercial con fines academicosAttribution-NonCommercial-NoDerivatives 4.0 Internacionalreponame:Repositorio Institucional Abierto (UTN)instname:Universidad Tecnológica Nacional2025-09-11T10:50:06Zoai:ria.utn.edu.ar:20.500.12272/3323instacron:UTNInstitucionalhttp://ria.utn.edu.ar/Universidad públicaNo correspondehttp://ria.utn.edu.ar/oaigestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:a2025-09-11 10:50:06.41Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacionalfalse
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
Rottoli, Giovanni Daián
Knowledge discovery process
Spatial clustering
Regionalization
Decision tree learning
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 Rottoli, Giovanni Daián
Merlino, Hernán Daniel
García Martínez, Ramón
author Rottoli, Giovanni Daián
author_facet Rottoli, Giovanni Daián
Merlino, Hernán Daniel
García Martínez, Ramón
author_role author
author2 Merlino, Hernán Daniel
García Martínez, Ramón
author2_role author
author
dc.subject.none.fl_str_mv Knowledge discovery process
Spatial clustering
Regionalization
Decision tree learning
Spatial data mining
topic Knowledge discovery process
Spatial clustering
Regionalization
Decision tree learning
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 spat ial clustering algorithm on real data are also provided.
Fil: Rottoli, Giovanni Daián. Universidad Nacional de Lanús; Argentina.
Fil: Merlino, Hernán Daniel. Universidad Nacional de Lanús; Argentina.
Fil: García Martínez, Ramón. Universidad Nacional de Lanús. Departamento Desarrollo Productivo y Tecnológico. Grupo de Investigación en Sistemas de Información; Argentina.
Fil: Rottoli, Giovanni Daián. Universidad Tecnológica Nacional. Facultad Regional Concepción del Uruguay. Departamento Ingeniería en Sistemas de Información. Grupo de Investigación en Bases de Datos; Argentina.
Fil: Rottoli, Giovanni Daián. Universidad Nacional de La Plata; Argentina.
Fil: García Martínez, Ramón. Comisión de Investigaciones Científicas; Argentina.
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 spat ial clustering algorithm on real data are also provided.
publishDate 2017
dc.date.none.fl_str_mv 2017
2018-12-13T11:47:04Z
2018-12-13T11:47:04Z
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv International Conference on Software Engineering & Knowledge Engineering. Ed. USA KSI Research Inc. and Knowledge Systems Institute, 410415 (2017)
http://hdl.handle.net/20.500.12272/3323
10.18293/SEKE2017-013
identifier_str_mv International Conference on Software Engineering & Knowledge Engineering. Ed. USA KSI Research Inc. and Knowledge Systems Institute, 410415 (2017)
10.18293/SEKE2017-013
url http://hdl.handle.net/20.500.12272/3323
dc.language.none.fl_str_mv eng
eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0/
Rottoli, Giovanni Daián ; Merlino, Hernán Daniel ; García Martínez, Ramón
No comercial con fines academicos
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
Rottoli, Giovanni Daián ; Merlino, Hernán Daniel ; García Martínez, Ramón
No comercial con fines academicos
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.source.none.fl_str_mv reponame:Repositorio Institucional Abierto (UTN)
instname:Universidad Tecnológica Nacional
reponame_str Repositorio Institucional Abierto (UTN)
collection Repositorio Institucional Abierto (UTN)
instname_str Universidad Tecnológica Nacional
repository.name.fl_str_mv Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacional
repository.mail.fl_str_mv gestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.ar
_version_ 1842976654816706560
score 12.993085

Publicaciones similares