Knowledge discovery process for detection of spatial outliers

Autores
Rottoli, Giovanni Daián; Merlino, Hernán Daniel; García Martínez, Ramón
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Detection of spatial outliers is a spatial data mining task aimed at discovering data observations that differ from other data observations within its spatial neighborhood. Some considerations that depend on the problem domain and data characteristics have to be taken into account for the selection of the data mining algorithms to be used in each data mining project. This massive amount of possible algorithm combinations makes it necessary to design a knowledge discovery process for detection of local spatial outliers in order to perform this activity in a standardized way. This work provides a proposal for this knowledge discovery process based on the Knowledge Discovery in Database process (KDD) and a proof of concept of this design using real world data.
Fil: Rottoli, Giovanni Daián. Universidad Nacional de Lanús; Argentina.
Fil: Merlino, Hernán Daniel. Universidad Nacional de Lanús; Argentina.
Fil: García Martinez, Ramón. Universidad Nacional de Lanús; Argentina. CIC Bs As; 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.
Peer Reviewed
Materia
Spatial outliers
Local outliers
Spatial data mining
Knowledge discovery process
Spatial clustering
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:20.500.12272/3309
Acceder
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network_acronym_str RIAUTN
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network_name_str Repositorio Institucional Abierto (UTN)
spelling Knowledge discovery process for detection of spatial outliersRottoli, Giovanni DaiánMerlino, Hernán DanielGarcía Martínez, RamónSpatial outliersLocal outliersSpatial data miningKnowledge discovery processSpatial clusteringDetection of spatial outliers is a spatial data mining task aimed at discovering data observations that differ from other data observations within its spatial neighborhood. Some considerations that depend on the problem domain and data characteristics have to be taken into account for the selection of the data mining algorithms to be used in each data mining project. This massive amount of possible algorithm combinations makes it necessary to design a knowledge discovery process for detection of local spatial outliers in order to perform this activity in a standardized way. This work provides a proposal for this knowledge discovery process based on the Knowledge Discovery in Database process (KDD) and a proof of concept of this design using real world data.Fil: Rottoli, Giovanni Daián. Universidad Nacional de Lanús; Argentina.Fil: Merlino, Hernán Daniel. Universidad Nacional de Lanús; Argentina.Fil: García Martinez, Ramón. Universidad Nacional de Lanús; Argentina. CIC Bs As; ArgentinaFil: 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.Peer Reviewed2018-12-05T01:30:42Z2018-12-05T01:30:42Z2018info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfRecent Trends and Future Technology in Applied Intelligence. IEA/AIE 2018. Lecture Notes in Computer Science 10868: 57-68 (2018)http://hdl.handle.net/20.500.12272/3309https://doi.org/10.1007/978-3-319-92058-0_6enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/Rottoli, Giovanni Daián ; Merlino, Hernán Daniel ; García Martinez, RamónNo comercial con fines académicosAttribution-NonCommercial-NoDerivatives 4.0 Internacionalreponame:Repositorio Institucional Abierto (UTN)instname:Universidad Tecnológica Nacional2025-09-29T14:29:34Zoai:ria.utn.edu.ar:20.500.12272/3309instacron: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:a2025-09-29 14:29:34.553Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacionalfalse
dc.title.none.fl_str_mv Knowledge discovery process for detection of spatial outliers
title Knowledge discovery process for detection of spatial outliers
spellingShingle Knowledge discovery process for detection of spatial outliers
Rottoli, Giovanni Daián
Spatial outliers
Local outliers
Spatial data mining
Knowledge discovery process
Spatial clustering
title_short Knowledge discovery process for detection of spatial outliers
title_full Knowledge discovery process for detection of spatial outliers
title_fullStr Knowledge discovery process for detection of spatial outliers
title_full_unstemmed Knowledge discovery process for detection of spatial outliers
title_sort Knowledge discovery process for detection of spatial outliers
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 Spatial outliers
Local outliers
Spatial data mining
Knowledge discovery process
Spatial clustering
topic Spatial outliers
Local outliers
Spatial data mining
Knowledge discovery process
Spatial clustering
dc.description.none.fl_txt_mv Detection of spatial outliers is a spatial data mining task aimed at discovering data observations that differ from other data observations within its spatial neighborhood. Some considerations that depend on the problem domain and data characteristics have to be taken into account for the selection of the data mining algorithms to be used in each data mining project. This massive amount of possible algorithm combinations makes it necessary to design a knowledge discovery process for detection of local spatial outliers in order to perform this activity in a standardized way. This work provides a proposal for this knowledge discovery process based on the Knowledge Discovery in Database process (KDD) and a proof of concept of this design using real world data.
Fil: Rottoli, Giovanni Daián. Universidad Nacional de Lanús; Argentina.
Fil: Merlino, Hernán Daniel. Universidad Nacional de Lanús; Argentina.
Fil: García Martinez, Ramón. Universidad Nacional de Lanús; Argentina. CIC Bs As; 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.
Peer Reviewed
description Detection of spatial outliers is a spatial data mining task aimed at discovering data observations that differ from other data observations within its spatial neighborhood. Some considerations that depend on the problem domain and data characteristics have to be taken into account for the selection of the data mining algorithms to be used in each data mining project. This massive amount of possible algorithm combinations makes it necessary to design a knowledge discovery process for detection of local spatial outliers in order to perform this activity in a standardized way. This work provides a proposal for this knowledge discovery process based on the Knowledge Discovery in Database process (KDD) and a proof of concept of this design using real world data.
publishDate 2018
dc.date.none.fl_str_mv 2018-12-05T01:30:42Z
2018-12-05T01:30:42Z
2018
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv Recent Trends and Future Technology in Applied Intelligence. IEA/AIE 2018. Lecture Notes in Computer Science 10868: 57-68 (2018)
http://hdl.handle.net/20.500.12272/3309
https://doi.org/10.1007/978-3-319-92058-0_6
identifier_str_mv Recent Trends and Future Technology in Applied Intelligence. IEA/AIE 2018. Lecture Notes in Computer Science 10868: 57-68 (2018)
url http://hdl.handle.net/20.500.12272/3309
https://doi.org/10.1007/978-3-319-92058-0_6
dc.language.none.fl_str_mv 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 Martinez, Ramón
No comercial con fines académicos
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 Martinez, Ramón
No comercial con fines académicos
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
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score 12.558318

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