Co-location rules discovery process focused on reference spatial features using decision tree learning

Autores
Rottoli, Giovanni Daián; Merlino, Hernán Daniel; García Martínez, Ramón
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The co-location discovery process serves to find subsets of spatial features frequently located together. Many algorithms and methods have been designed in recent years; however, finding this kind of patterns around specific spatial features is a task in which the existing solutions provide incorrect results. Throughout this paper we propose a knowledge discovery process to find co-location patterns focused on reference features using decision tree learning algorithms on transactional data generated using maximal cliques. A validation test of this process is provided.
Fil: Merlino, Hernán Daniel. Universidad Nacional de Lanús; 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: Rottoli, Giovanni Daián. 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: García Martínez, Ramón. Comisión de Investigaciones Científicas; Argentina.
Peer Reviewed
Materia
Co-location patterns
Spatial data mining
Decision trees algorithms
Maximal cliques
Knowledge discovery process
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:20.500.12272/3308
Acceder
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oai_identifier_str oai:ria.utn.edu.ar:20.500.12272/3308
network_acronym_str RIAUTN
repository_id_str a
network_name_str Repositorio Institucional Abierto (UTN)
spelling Co-location rules discovery process focused on reference spatial features using decision tree learningRottoli, Giovanni DaiánMerlino, Hernán DanielGarcía Martínez, RamónCo-location patternsSpatial data miningDecision trees algorithmsMaximal cliquesKnowledge discovery processThe co-location discovery process serves to find subsets of spatial features frequently located together. Many algorithms and methods have been designed in recent years; however, finding this kind of patterns around specific spatial features is a task in which the existing solutions provide incorrect results. Throughout this paper we propose a knowledge discovery process to find co-location patterns focused on reference features using decision tree learning algorithms on transactional data generated using maximal cliques. A validation test of this process is provided.Fil: Merlino, Hernán Daniel. Universidad Nacional de Lanús; 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: Rottoli, Giovanni Daián. 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: García Martínez, Ramón. Comisión de Investigaciones Científicas; Argentina.Peer Reviewed2018-11-30T23:43:53Z2018-11-30T23:43:53Z2017info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfAdvances in Artificial Intelligence: From Theory to Practice 10350: 221-226 (2017)http://hdl.handle.net/20.500.12272/3308https://doi.org/10.1007/978-3-319-60042-0_25enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/Rottoli, Giovanni Daián ; Merlino, Hernán Daniel ; García Martínez, RamónNo comercial con fines académicosAttribution-NonCommercial-NoDerivatives 4.0 Internacionalreponame:Repositorio Institucional Abierto (UTN)instname:Universidad Tecnológica Nacional2025-09-04T11:14:39Zoai:ria.utn.edu.ar:20.500.12272/3308instacron: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 11:14:39.528Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacionalfalse
dc.title.none.fl_str_mv Co-location rules discovery process focused on reference spatial features using decision tree learning
title Co-location rules discovery process focused on reference spatial features using decision tree learning
spellingShingle Co-location rules discovery process focused on reference spatial features using decision tree learning
Rottoli, Giovanni Daián
Co-location patterns
Spatial data mining
Decision trees algorithms
Maximal cliques
Knowledge discovery process
title_short Co-location rules discovery process focused on reference spatial features using decision tree learning
title_full Co-location rules discovery process focused on reference spatial features using decision tree learning
title_fullStr Co-location rules discovery process focused on reference spatial features using decision tree learning
title_full_unstemmed Co-location rules discovery process focused on reference spatial features using decision tree learning
title_sort Co-location rules discovery process focused on reference spatial features using decision tree learning
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 Co-location patterns
Spatial data mining
Decision trees algorithms
Maximal cliques
Knowledge discovery process
topic Co-location patterns
Spatial data mining
Decision trees algorithms
Maximal cliques
Knowledge discovery process
dc.description.none.fl_txt_mv The co-location discovery process serves to find subsets of spatial features frequently located together. Many algorithms and methods have been designed in recent years; however, finding this kind of patterns around specific spatial features is a task in which the existing solutions provide incorrect results. Throughout this paper we propose a knowledge discovery process to find co-location patterns focused on reference features using decision tree learning algorithms on transactional data generated using maximal cliques. A validation test of this process is provided.
Fil: Merlino, Hernán Daniel. Universidad Nacional de Lanús; 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: Rottoli, Giovanni Daián. 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: García Martínez, Ramón. Comisión de Investigaciones Científicas; Argentina.
Peer Reviewed
description The co-location discovery process serves to find subsets of spatial features frequently located together. Many algorithms and methods have been designed in recent years; however, finding this kind of patterns around specific spatial features is a task in which the existing solutions provide incorrect results. Throughout this paper we propose a knowledge discovery process to find co-location patterns focused on reference features using decision tree learning algorithms on transactional data generated using maximal cliques. A validation test of this process is provided.
publishDate 2017
dc.date.none.fl_str_mv 2017
2018-11-30T23:43:53Z
2018-11-30T23:43:53Z
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 Advances in Artificial Intelligence: From Theory to Practice 10350: 221-226 (2017)
http://hdl.handle.net/20.500.12272/3308
https://doi.org/10.1007/978-3-319-60042-0_25
identifier_str_mv Advances in Artificial Intelligence: From Theory to Practice 10350: 221-226 (2017)
url http://hdl.handle.net/20.500.12272/3308
https://doi.org/10.1007/978-3-319-60042-0_25
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 Martínez, 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 Martínez, 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_ 1842344356452761600
score 12.623145

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