Using ensembles with spatial clustering approaches applied in the delineation of management classes in precision agriculture.
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
Texto Completo: | http://www.alice.cnptia.embrapa.br/alice/handle/doc/1099223 |
Resumo: | 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. |
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Using ensembles with spatial clustering approaches applied in the delineation of management classes in precision agriculture.Classes de manejoAgrupamento de dados espaciaisEnsemblesCllusterizaçãoAgricultura de PrecisãoPrecision agricultureSpatial dataCluster analysisThis 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.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.EDUARDO ANTONIO SPERANZA, CNPTIA; RICARDO RODRIGUES CIFERRI, UFSCar.SPERANZA, E. A.CIFERRI, R. R.2018-11-13T23:58:24Z2018-11-13T23:58:24Z2018-11-1320172020-01-21T11:11:11Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleBrazilian Journal of Cartography, Rio de Janeiro, v. 69, n. 5, p. 923-935, maio 2017.http://www.alice.cnptia.embrapa.br/alice/handle/doc/1099223enginfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPA2018-11-13T23:58:30Zoai:www.alice.cnptia.embrapa.br:doc/1099223Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestopendoar:21542018-11-13T23:58:30falseRepositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542018-11-13T23:58:30Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false |
dc.title.none.fl_str_mv |
Using ensembles with spatial clustering approaches applied in the delineation of management classes in precision agriculture. |
title |
Using ensembles with spatial clustering approaches applied in the delineation of management classes in precision agriculture. |
spellingShingle |
Using ensembles with spatial clustering approaches applied in the delineation of management classes in precision agriculture. SPERANZA, E. A. Classes de manejo Agrupamento de dados espaciais Ensembles Cllusterização Agricultura de Precisão Precision agriculture Spatial data Cluster analysis |
title_short |
Using ensembles with spatial clustering approaches applied in the delineation of management classes in precision agriculture. |
title_full |
Using ensembles with spatial clustering approaches applied in the delineation of management classes in precision agriculture. |
title_fullStr |
Using ensembles with spatial clustering approaches applied in the delineation of management classes in precision agriculture. |
title_full_unstemmed |
Using ensembles with spatial clustering approaches applied in the delineation of management classes in precision agriculture. |
title_sort |
Using ensembles with spatial clustering approaches applied in the delineation of management classes in precision agriculture. |
author |
SPERANZA, E. A. |
author_facet |
SPERANZA, E. A. CIFERRI, R. R. |
author_role |
author |
author2 |
CIFERRI, R. R. |
author2_role |
author |
dc.contributor.none.fl_str_mv |
EDUARDO ANTONIO SPERANZA, CNPTIA; RICARDO RODRIGUES CIFERRI, UFSCar. |
dc.contributor.author.fl_str_mv |
SPERANZA, E. A. CIFERRI, R. R. |
dc.subject.por.fl_str_mv |
Classes de manejo Agrupamento de dados espaciais Ensembles Cllusterização Agricultura de Precisão Precision agriculture Spatial data Cluster analysis |
topic |
Classes de manejo Agrupamento de dados espaciais Ensembles Cllusterização Agricultura de Precisão Precision agriculture Spatial data Cluster analysis |
description |
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. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017 2018-11-13T23:58:24Z 2018-11-13T23:58:24Z 2018-11-13 2020-01-21T11:11:11Z |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
Brazilian Journal of Cartography, Rio de Janeiro, v. 69, n. 5, p. 923-935, maio 2017. http://www.alice.cnptia.embrapa.br/alice/handle/doc/1099223 |
identifier_str_mv |
Brazilian Journal of Cartography, Rio de Janeiro, v. 69, n. 5, p. 923-935, maio 2017. |
url |
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1099223 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa) instacron:EMBRAPA |
instname_str |
Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
instacron_str |
EMBRAPA |
institution |
EMBRAPA |
reponame_str |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
collection |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
repository.name.fl_str_mv |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
repository.mail.fl_str_mv |
cg-riaa@embrapa.br |
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1794503464757231616 |