02179naa a2200241 a 450000100080000000500110000800800410001910000200006024501530008026000090023350002500024252011510049265000210164365000260166465000170169065000290170765300350173665300220177165300210179365300140181470000190182877300900184720992232020-01-21       2017    bl uuuu     u00u1 u    #d1 aSPERANZA, E. A.  aUsing ensembles with spatial clustering approaches applied in the delineation of management classes in precision agriculture.h[electronic resource]  c2017  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.  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.  aCluster analysis  aPrecision agriculture  aSpatial data  aAgricultura de Precisão  aAgrupamento de dados espaciais  aClasses de manejo  aCllusterização  aEnsembles1 aCIFERRI, R. R.  tBrazilian Journal of Cartography, Rio de Janeirogv. 69, n. 5, p. 923-935, maio 2017.