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Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
08/11/2017 |
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Data da última atualização: |
21/01/2020 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
SPERANZA, E. A.; CIFERRI, R. R.; CIFERRI, C. D. de A. |
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Afiliação: |
EDUARDO ANTONIO SPERANZA, CNPTIA; RICARDO R. CIFERRI, UFSCar; CRISTINA DUTRA DE AGUIAR CIFERRI, ICMC/USP. |
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Título: |
Clustering approaches and ensembles applied in the delineation of management classes in precision agriculture. |
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Ano de publicação: |
2016 |
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Fonte/Imprenta: |
In: BRAZILIAN SYMPOSIUM ON GEOINFORMATICS, 17., 2016, Campos do Jordão. Proceedings... São José dos Campos: INPE, 2016. |
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Páginas: |
p. 152-165. |
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Idioma: |
Inglês |
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Notas: |
Geoinfo 2016. |
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Conteúdo: |
Abstract. This paper describes an experiment performed using different 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 this context, and from the hierarchical clustering algorithm HACCSpatial, especially designed for this PA task. We also performed experiments using traditional ensembles approaches from the literature, evaluating their behavior to achieve consensus solutions from individual clusterings obtained from features splitting or running one of the abovementioned algorithms. Results showed some differences between FCM and HACC-Spatial, mainly for the visualization of management classes in the form of maps. Considering the consensus clusterings provided by ensembles, it became clear the attempt to achieve an agreement result that most closely matches the original clusterings, showing us some details that may go undetected when we analyse only the individual clusterings. |
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Palavras-Chave: |
Fuzzy c-Means algorithm; Spatial hierarchical clustering algorithm. |
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Thesagro: |
Agricultura de precisão. |
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Thesaurus NAL: |
Cluster analysis; Fuzzy logic; Precision agriculture; Spatial data. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/166376/1/PL-Speranzaetal-Geoinfo2016.pdf
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Marc: |
LEADER 01938nam a2200241 a 4500
001 2079181
005 2020-01-21
008 2016 bl uuuu u00u1 u #d
100 1 $aSPERANZA, E. A.
245 $aClustering approaches and ensembles applied in the delineation of management classes in precision agriculture.$h[electronic resource]
260 $aIn: BRAZILIAN SYMPOSIUM ON GEOINFORMATICS, 17., 2016, Campos do Jordão. Proceedings... São José dos Campos: INPE$c2016
300 $ap. 152-165.
500 $aGeoinfo 2016.
520 $aAbstract. This paper describes an experiment performed using different 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 this context, and from the hierarchical clustering algorithm HACCSpatial, especially designed for this PA task. We also performed experiments using traditional ensembles approaches from the literature, evaluating their behavior to achieve consensus solutions from individual clusterings obtained from features splitting or running one of the abovementioned algorithms. Results showed some differences between FCM and HACC-Spatial, mainly for the visualization of management classes in the form of maps. Considering the consensus clusterings provided by ensembles, it became clear the attempt to achieve an agreement result that most closely matches the original clusterings, showing us some details that may go undetected when we analyse only the individual clusterings.
650 $aCluster analysis
650 $aFuzzy logic
650 $aPrecision agriculture
650 $aSpatial data
650 $aAgricultura de precisão
653 $aFuzzy c-Means algorithm
653 $aSpatial hierarchical clustering algorithm
700 1 $aCIFERRI, R. R.
700 1 $aCIFERRI, C. D. de A.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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