Spatial association discovery process using frequent subgraph mining

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 entail...

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Detalles Bibliográficos
Autores principales:	Rottoli, Giovanni Daián, Merlino, Hernán
Formato:	Articulo
Lenguaje:	Inglés
Publicado:	2020
Materias:	Informática Frequent subgraph mining SARM Spatial association mining Spatial data mining Spatial knowledge discovery
Acceso en línea:	http://sedici.unlp.edu.ar/handle/10915/141906
Aporte de:	SEDICI (UNLP) de Universidad Nacional de La Plata

id	I19-R120-10915-141906
record_format	dspace
institution	Universidad Nacional de La Plata
institution_str	I-19
repository_str	R-120
collection	SEDICI (UNLP)
language	Inglés
topic	Informática Frequent subgraph mining SARM Spatial association mining Spatial data mining Spatial knowledge discovery
spellingShingle	Informática Frequent subgraph mining SARM Spatial association mining Spatial data mining Spatial knowledge discovery Rottoli, Giovanni Daián Merlino, Hernán Spatial association discovery process using frequent subgraph mining
topic_facet	Informática Frequent subgraph mining SARM Spatial association mining Spatial data mining Spatial knowledge discovery
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.
format	Articulo Articulo
author	Rottoli, Giovanni Daián Merlino, Hernán
author_facet	Rottoli, Giovanni Daián Merlino, Hernán
author_sort	Rottoli, Giovanni Daián
title	Spatial association discovery process using frequent subgraph mining
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
publishDate	2020
url	http://sedici.unlp.edu.ar/handle/10915/141906
work_keys_str_mv	AT rottoligiovannidaian spatialassociationdiscoveryprocessusingfrequentsubgraphmining AT merlinohernan spatialassociationdiscoveryprocessusingfrequentsubgraphmining
bdutipo_str	Repositorios
_version_	1764820458488725504

Spatial association discovery process using frequent subgraph mining

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