Spatial association discovery process using frequent subgraph mining
- Autores
- Rottoli, Giovanni Daián; Merlino, Hernán
- Año de publicación
- 2020
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Spatial associations are one of the most relevant kinds of patterns used by business intelligence regarding spatial data. Due to the characteristics of this particular type of information, different approaches have been proposed for spatial association mining. This wide variety of methods has entailed the need for a process to integrate the activities for association discovery, one that is easy to implement and flexible enough to be adapted to any particular situation, particularly for small and medium-size projects to guide the useful pattern discovery process. Thus, this work proposes an adaptable knowledge discovery process that uses graph theory to model different spatial relationships from multiple scenarios, and frequent subgraph mining to discover spatial associations. A proof of concept is presented using real data.
Facultad de Informática - Materia
-
Informática
Frequent subgraph mining
SARM
Spatial association mining
Spatial data mining
Spatial knowledge discovery - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-sa/4.0/
- Repositorio
.jpg)
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/141906
Ver los metadatos del registro completo
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Spatial association discovery process using frequent subgraph miningRottoli, Giovanni DaiánMerlino, HernánInformáticaFrequent subgraph miningSARMSpatial association miningSpatial data miningSpatial knowledge discoverySpatial associations are one of the most relevant kinds of patterns used by business intelligence regarding spatial data. Due to the characteristics of this particular type of information, different approaches have been proposed for spatial association mining. This wide variety of methods has entailed the need for a process to integrate the activities for association discovery, one that is easy to implement and flexible enough to be adapted to any particular situation, particularly for small and medium-size projects to guide the useful pattern discovery process. Thus, this work proposes an adaptable knowledge discovery process that uses graph theory to model different spatial relationships from multiple scenarios, and frequent subgraph mining to discover spatial associations. A proof of concept is presented using real data.Facultad de Informática2020-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf1884-1891http://sedici.unlp.edu.ar/handle/10915/141906enginfo:eu-repo/semantics/altIdentifier/issn/1693-6930info:eu-repo/semantics/altIdentifier/issn/2302-9293info:eu-repo/semantics/altIdentifier/issn/2087-278Xinfo:eu-repo/semantics/altIdentifier/doi/10.12928/telkomnika.v18i4.13858info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-sa/4.0/Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T11:04:42Zoai:sedici.unlp.edu.ar:10915/141906Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 11:04:43.222SEDICI (UNLP) - Universidad Nacional de La Platafalse |
| dc.title.none.fl_str_mv |
Spatial association discovery process using frequent subgraph mining |
| title |
Spatial association discovery process using frequent subgraph mining |
| spellingShingle |
Spatial association discovery process using frequent subgraph mining Rottoli, Giovanni Daián Informática Frequent subgraph mining SARM Spatial association mining Spatial data mining Spatial knowledge discovery |
| title_short |
Spatial association discovery process using frequent subgraph mining |
| title_full |
Spatial association discovery process using frequent subgraph mining |
| title_fullStr |
Spatial association discovery process using frequent subgraph mining |
| title_full_unstemmed |
Spatial association discovery process using frequent subgraph mining |
| title_sort |
Spatial association discovery process using frequent subgraph mining |
| dc.creator.none.fl_str_mv |
Rottoli, Giovanni Daián Merlino, Hernán |
| author |
Rottoli, Giovanni Daián |
| author_facet |
Rottoli, Giovanni Daián Merlino, Hernán |
| author_role |
author |
| author2 |
Merlino, Hernán |
| author2_role |
author |
| dc.subject.none.fl_str_mv |
Informática Frequent subgraph mining SARM Spatial association mining Spatial data mining Spatial knowledge discovery |
| topic |
Informática Frequent subgraph mining SARM Spatial association mining Spatial data mining Spatial knowledge discovery |
| dc.description.none.fl_txt_mv |
Spatial associations are one of the most relevant kinds of patterns used by business intelligence regarding spatial data. Due to the characteristics of this particular type of information, different approaches have been proposed for spatial association mining. This wide variety of methods has entailed the need for a process to integrate the activities for association discovery, one that is easy to implement and flexible enough to be adapted to any particular situation, particularly for small and medium-size projects to guide the useful pattern discovery process. Thus, this work proposes an adaptable knowledge discovery process that uses graph theory to model different spatial relationships from multiple scenarios, and frequent subgraph mining to discover spatial associations. A proof of concept is presented using real data. Facultad de Informática |
| description |
Spatial associations are one of the most relevant kinds of patterns used by business intelligence regarding spatial data. Due to the characteristics of this particular type of information, different approaches have been proposed for spatial association mining. This wide variety of methods has entailed the need for a process to integrate the activities for association discovery, one that is easy to implement and flexible enough to be adapted to any particular situation, particularly for small and medium-size projects to guide the useful pattern discovery process. Thus, this work proposes an adaptable knowledge discovery process that uses graph theory to model different spatial relationships from multiple scenarios, and frequent subgraph mining to discover spatial associations. A proof of concept is presented using real data. |
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2020 |
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2020-08 |
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eng |
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