Knowledge discovery process for description of spatially referenced clusters

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Spatial clustering is an important field of spatial data mining and knowledge discovery that serves to partition a spatial data set to obtain disjoint subsets with spatial elements that are similar to each other. Existing algorithms can be used to perform three types of cluster analyses, including c...

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Detalles Bibliográficos
Autores principales:	Rottoli, Giovanni Daián, Merlino, Hernán Daniel, García Martínez, Ramón
Formato:	Documento de conferencia publishedVersion
Lenguaje:	Inglés Inglés
Publicado:	2018
Materias:	Knowledge discovery process Spatial clustering Regionalization Decision tree learning Spatial data mining
Acceso en línea:	http://hdl.handle.net/20.500.12272/3323
Aporte de:	RIA - Repositorio Institucional Abierto (UTN) de Universidad Tecnológica Nacional

id	I68-R174-20.500.12272-3323
record_format	dspace
institution	Universidad Tecnológica Nacional
institution_str	I-68
repository_str	R-174
collection	RIA - Repositorio Institucional Abierto (UTN)
language	Inglés Inglés
topic	Knowledge discovery process Spatial clustering Regionalization Decision tree learning Spatial data mining
spellingShingle	Knowledge discovery process Spatial clustering Regionalization Decision tree learning Spatial data mining Rottoli, Giovanni Daián Merlino, Hernán Daniel García Martínez, Ramón Knowledge discovery process for description of spatially referenced clusters
topic_facet	Knowledge discovery process Spatial clustering Regionalization Decision tree learning Spatial data mining
description	Spatial clustering is an important field of spatial data mining and knowledge discovery that serves to partition a spatial data set to obtain disjoint subsets with spatial elements that are similar to each other. Existing algorithms can be used to perform three types of cluster analyses, including clustering of spatial points, regionalization and point pattern analysis. However, all these existing methods do not provide a description of the discovered spatial clusters, which is useful for decision making in many different fields. This work proposes a knowledge discovery process for the description of spatially referenced clusters that uses decision tree learning algorithms. Two proofs of concept of the proposed process using different spat ial clustering algorithm on real data are also provided.
format	Documento de conferencia publishedVersion Documento de conferencia
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	Knowledge discovery process for description of spatially referenced clusters
title_short	Knowledge discovery process for description of spatially referenced clusters
title_full	Knowledge discovery process for description of spatially referenced clusters
title_fullStr	Knowledge discovery process for description of spatially referenced clusters
title_full_unstemmed	Knowledge discovery process for description of spatially referenced clusters
title_sort	knowledge discovery process for description of spatially referenced clusters
publishDate	2018
url	http://hdl.handle.net/20.500.12272/3323
work_keys_str_mv	AT rottoligiovannidaian knowledgediscoveryprocessfordescriptionofspatiallyreferencedclusters AT merlinohernandaniel knowledgediscoveryprocessfordescriptionofspatiallyreferencedclusters AT garciamartinezramon knowledgediscoveryprocessfordescriptionofspatiallyreferencedclusters
bdutipo_str	Repositorios
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Knowledge discovery process for description of spatially referenced clusters

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