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
- Institución
- Universidad Tecnológica Nacional
- OAI Identificador
- oai:ria.utn.edu.ar:20.500.12272/3309
Ver los metadatos del registro completo
id |
RIAUTN_5fa9b3c42e6298516e7d9d6cbc83356e |
---|---|
oai_identifier_str |
oai:ria.utn.edu.ar:20.500.12272/3309 |
network_acronym_str |
RIAUTN |
repository_id_str |
a |
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 Nacional2025-09-04T11:14:37Zoai: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:a2025-09-04 11:14:37.629Repositorio 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 |
_version_ |
1842344355942105088 |
score |
12.623145 |