Clustering approaches and ensembles applied in the delineation of management classes in precision agriculture.

Clustering approaches and ensembles applied in the delineation of management classes in precision agriculture.

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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Bibliographic Details
Main Authors: SPERANZA, E. A., CIFERRI, R. R., CIFERRI, C. D. de A.
Other Authors: EDUARDO ANTONIO SPERANZA, CNPTIA; RICARDO R. CIFERRI, UFSCar; CRISTINA DUTRA DE AGUIAR CIFERRI, ICMC/USP.
Format: Anais e Proceedings de eventos biblioteca
Language:English
eng
Published: 2017-11-08
Subjects:Fuzzy c-Means algorithm, Spatial hierarchical clustering algorithm, Agricultura de precisão, Precision agriculture, Cluster analysis, Fuzzy logic, Spatial data,
Online Access:http://www.alice.cnptia.embrapa.br/alice/handle/doc/1079181
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spelling dig-alice-doc-10791812017-11-09T18:15:20Z Clustering approaches and ensembles applied in the delineation of management classes in precision agriculture. SPERANZA, E. A. CIFERRI, R. R. CIFERRI, C. D. de A. EDUARDO ANTONIO SPERANZA, CNPTIA; RICARDO R. CIFERRI, UFSCar; CRISTINA DUTRA DE AGUIAR CIFERRI, ICMC/USP. Fuzzy c-Means algorithm Spatial hierarchical clustering algorithm Agricultura de precisão Precision agriculture Cluster analysis Fuzzy logic Spatial data 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. Geoinfo 2016. 2017-11-09T18:08:48Z 2017-11-09T18:08:48Z 2017-11-08 2016 2020-01-21T11:11:11Z Anais e Proceedings de eventos In: BRAZILIAN SYMPOSIUM ON GEOINFORMATICS, 17., 2016, Campos do Jordão. Proceedings... São José dos Campos: INPE, 2016. http://www.alice.cnptia.embrapa.br/alice/handle/doc/1079181 en eng openAccess p. 152-165.
institution EMBRAPA
collection DSpace
country Brasil
countrycode BR
component Bibliográfico
access En linea
databasecode dig-alice
tag biblioteca
region America del Sur
libraryname Sistema de bibliotecas de EMBRAPA
language English
eng
topic Fuzzy c-Means algorithm
Spatial hierarchical clustering algorithm
Agricultura de precisão
Precision agriculture
Cluster analysis
Fuzzy logic
Spatial data
Fuzzy c-Means algorithm
Spatial hierarchical clustering algorithm
Agricultura de precisão
Precision agriculture
Cluster analysis
Fuzzy logic
Spatial data
spellingShingle Fuzzy c-Means algorithm
Spatial hierarchical clustering algorithm
Agricultura de precisão
Precision agriculture
Cluster analysis
Fuzzy logic
Spatial data
Fuzzy c-Means algorithm
Spatial hierarchical clustering algorithm
Agricultura de precisão
Precision agriculture
Cluster analysis
Fuzzy logic
Spatial data
SPERANZA, E. A.
CIFERRI, R. R.
CIFERRI, C. D. de A.
Clustering approaches and ensembles applied in the delineation of management classes in precision agriculture.
description 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.
author2 EDUARDO ANTONIO SPERANZA, CNPTIA; RICARDO R. CIFERRI, UFSCar; CRISTINA DUTRA DE AGUIAR CIFERRI, ICMC/USP.
author_facet EDUARDO ANTONIO SPERANZA, CNPTIA; RICARDO R. CIFERRI, UFSCar; CRISTINA DUTRA DE AGUIAR CIFERRI, ICMC/USP.
SPERANZA, E. A.
CIFERRI, R. R.
CIFERRI, C. D. de A.
format Anais e Proceedings de eventos
topic_facet Fuzzy c-Means algorithm
Spatial hierarchical clustering algorithm
Agricultura de precisão
Precision agriculture
Cluster analysis
Fuzzy logic
Spatial data
author SPERANZA, E. A.
CIFERRI, R. R.
CIFERRI, C. D. de A.
author_sort SPERANZA, E. A.
title Clustering approaches and ensembles applied in the delineation of management classes in precision agriculture.
title_short Clustering approaches and ensembles applied in the delineation of management classes in precision agriculture.
title_full Clustering approaches and ensembles applied in the delineation of management classes in precision agriculture.
title_fullStr Clustering approaches and ensembles applied in the delineation of management classes in precision agriculture.
title_full_unstemmed Clustering approaches and ensembles applied in the delineation of management classes in precision agriculture.
title_sort clustering approaches and ensembles applied in the delineation of management classes in precision agriculture.
publishDate 2017-11-08
url http://www.alice.cnptia.embrapa.br/alice/handle/doc/1079181
work_keys_str_mv AT speranzaea clusteringapproachesandensemblesappliedinthedelineationofmanagementclassesinprecisionagriculture
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