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

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 incorrec...

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Autores principales: Rottoli, Giovanni Daián, Merlino, Hernán Daniel, García Martínez, Ramón
Formato: Artículo publishedVersion
Lenguaje:Inglés
Publicado: 2018
Materias:
Co-location patterns
Spatial data mining
Decision trees algorithms
Maximal cliques
Knowledge discovery process
Acceso en línea:http://hdl.handle.net/20.500.12272/3308
https://doi.org/10.1007/978-3-319-60042-0_25
Aporte de:
RIA - Repositorio Institucional Abierto (UTN) de Universidad Tecnológica Nacional
id I68-R174-20.500.12272-3308
record_format dspace
institution Universidad Tecnológica Nacional
institution_str I-68
repository_str R-174
collection RIA - Repositorio Institucional Abierto (UTN)
language Inglés
topic Co-location patterns
Spatial data mining
Decision trees algorithms
Maximal cliques
Knowledge discovery process
spellingShingle Co-location patterns
Spatial data mining
Decision trees algorithms
Maximal cliques
Knowledge discovery process
Rottoli, Giovanni Daián
Merlino, Hernán Daniel
García Martínez, Ramón
Co-location rules discovery process focused on reference spatial features using decision tree learning
topic_facet Co-location patterns
Spatial data mining
Decision trees algorithms
Maximal cliques
Knowledge discovery process
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.
format Artículo
publishedVersion
Artículo
author Rottoli, Giovanni Daián
Merlino, Hernán Daniel
García Martínez, Ramón
author_facet Rottoli, Giovanni Daián
Merlino, Hernán Daniel
García Martínez, Ramón
author_sort Rottoli, Giovanni Daián
title Co-location rules discovery process focused on reference spatial features using decision tree learning
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
publishDate 2018
url http://hdl.handle.net/20.500.12272/3308
https://doi.org/10.1007/978-3-319-60042-0_25
work_keys_str_mv AT rottoligiovannidaian colocationrulesdiscoveryprocessfocusedonreferencespatialfeaturesusingdecisiontreelearning
AT merlinohernandaniel colocationrulesdiscoveryprocessfocusedonreferencespatialfeaturesusingdecisiontreelearning
AT garciamartinezramon colocationrulesdiscoveryprocessfocusedonreferencespatialfeaturesusingdecisiontreelearning
bdutipo_str Repositorios
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