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Registro Completo |
Biblioteca(s): |
Embrapa Agricultura Digital. |
Data corrente: |
13/11/2018 |
Data da última atualização: |
21/01/2020 |
Tipo da produção científica: |
Artigo em Anais de Congresso |
Autoria: |
SPERANZA, E. A.; CIFERRI, R. R. |
Afiliação: |
EDUARDO ANTONIO SPERANZA, CNPTIA; RICARDO RODRIGUES CIFERRI, UFSCar. |
Título: |
Using ensembles with spatial clustering approaches applied in the delineation of management classes in precision agriculture. |
Ano de publicação: |
2017 |
Fonte/Imprenta: |
Brazilian Journal of Cartography, Rio de Janeiro, v. 69, n. 5, p. 923-935, maio 2017. |
Idioma: |
Inglês |
Notas: |
Título equivalente em português: Utilizando ensembles com abordagens de agrupamento espacial para o delineamento de classes de manejo em agricultura de precisão. Edição especial de papers selecionados que foram apresentados no GEOINFO 2016. |
Conteúdo: |
This paper describes experiments performed using diff erent approaches for spatial data clustering, aiming to assist the delineation of management classes in Precision Agriculture (PA). These approaches were established from the partitional clustering algorithm Fuzzy c-Means (FCM), traditionally used in PA, and from the hierarchical clustering algorithm HACC-Spatial, especially designed for PA. We also performed experiments using diff erent clustering ensembles approaches, evaluating their behavior to achieve consensus solutions from individual clusterings obtained from attribute splitting or using exclusively FCM or HACC-Spatial. The achieved results exhibited some diff erences between FCM and HACC-Spatial, mainly for the visualization of management classes in the form of maps. The HACCSpatial algorithm achieved, in general, better results when compared to FCM and ensembles approaches. Regarding the consensus clusterings provided by ensembles, we can point out the attempt to achieve agreement results which most closely matches the original clusterings, decreasing or increasing the stratifi cation of the management classes maps. |
Palavras-Chave: |
Agrupamento de dados espaciais; Classes de manejo; Cllusterização; Ensembles. |
Thesagro: |
Agricultura de Precisão. |
Thesaurus Nal: |
Cluster analysis; Precision agriculture; Spatial data. |
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/185863/1/Using-ensembles-Speranza-Geoinfo-2017.pdf
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Marc: |
LEADER 02179naa a2200241 a 4500 001 2099223 005 2020-01-21 008 2017 bl uuuu u00u1 u #d 100 1 $aSPERANZA, E. A. 245 $aUsing ensembles with spatial clustering approaches applied in the delineation of management classes in precision agriculture.$h[electronic resource] 260 $c2017 500 $aTítulo equivalente em português: Utilizando ensembles com abordagens de agrupamento espacial para o delineamento de classes de manejo em agricultura de precisão. Edição especial de papers selecionados que foram apresentados no GEOINFO 2016. 520 $aThis paper describes experiments performed using diff erent approaches for spatial data clustering, aiming to assist the delineation of management classes in Precision Agriculture (PA). These approaches were established from the partitional clustering algorithm Fuzzy c-Means (FCM), traditionally used in PA, and from the hierarchical clustering algorithm HACC-Spatial, especially designed for PA. We also performed experiments using diff erent clustering ensembles approaches, evaluating their behavior to achieve consensus solutions from individual clusterings obtained from attribute splitting or using exclusively FCM or HACC-Spatial. The achieved results exhibited some diff erences between FCM and HACC-Spatial, mainly for the visualization of management classes in the form of maps. The HACCSpatial algorithm achieved, in general, better results when compared to FCM and ensembles approaches. Regarding the consensus clusterings provided by ensembles, we can point out the attempt to achieve agreement results which most closely matches the original clusterings, decreasing or increasing the stratifi cation of the management classes maps. 650 $aCluster analysis 650 $aPrecision agriculture 650 $aSpatial data 650 $aAgricultura de Precisão 653 $aAgrupamento de dados espaciais 653 $aClasses de manejo 653 $aCllusterização 653 $aEnsembles 700 1 $aCIFERRI, R. R. 773 $tBrazilian Journal of Cartography, Rio de Janeiro$gv. 69, n. 5, p. 923-935, maio 2017.
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Embrapa Agricultura Digital (CNPTIA) |
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