Data/hora: 11/06/2024 09:50:19

Biblioteca(s): 	Embrapa Territorial.	
Data corrente: 	10/01/2023	
Data da última atualização: 	10/01/2023	
Autoria: 	SILVA, M. A. S. da; MATOS, L. N.; SANTOS, F. E. DE O.; DOMPIERI, M. H. G.; MOURA, F. R. DE.	
Afiliação: 	MARCOS AURELIO SANTOS DA SILVA, CPATC; LEONARDO NOGUEIRA MATOS, UNIVERSIDADE FEDERAL DE SERGIPE; FLÁVIO EMANUEL DE OLIVEIRA SANTOS, UNIVERSIDADE FEDERAL DE SERGIPE; MARCIA HELENA GALINA DOMPIERI, CNPM; FÁBIO RODRIGUES DE MOURA, UNIVERSIDADE FEDERAL DE SERGIPE.	
Título: 	Feature extraction of spatial panel data with autoencoders for clustering the Brazilian agricultural diversity.	
Ano de publicação: 	2022	
Fonte/Imprenta: 	In: BRAZILIAN SYMPOSIUM ON GEOINFORMATICS, 22., 2022, São José dos Campos. Proceedings... São José dos Campos: MCTIC/INPE, 2022. p. 27-38.	
Idioma: 	Inglês	
Notas: 	GEOINFO 2022.	
Conteúdo: 	ABSTRACT - Brazilian agricultural production presents a high degree of spatial diversity, which challenges designing territorial public policies to promote sustainable development. This article proposes a new approach to cluster Brazilian municipalities according to their agricultural production. It combines a feature extraction mechanism using Deep Learning based on Autoencoders and clustering based on k-means and Self-Organizing Maps. We used the panel data from IBGE?s annual estimates of Brazilian agricultural production between 1999 and 2018. Different structures of simple stacked undercomplete autoencoders were analyzed, varying the number of layers and neurons in each of them, including the latent layer. We evaluated the asymmetric exponential linear loss function to cope with the sparse data. The results show that in comparison with the ground truth adopted, the autoencoder model combined with the Self-Organizing Maps and the k-means algorithm presented a better result than the clustering of the raw data from the k-means, demonstrating the ability of simple stacked autoencoders to reduce the dimensionality and create a new space of features in their latent layer where the data can be analyzed and clustered. Although the general accuracy is low, the results are promising, considering that we can add new improvements to the Deep Clustering process.	
Palavras-Chave: 	Clustering process.	
Categoria do assunto: 	P Recursos Naturais, Ciências Ambientais e da Terra	
URL: 	http://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1150828/1/6082.pdf	
Registro original: 	Embrapa Territorial (CNPM)	
Exemplares: 	CNPM - (6082-1) - UPC - AA - DD - 22/059 - AA - 2022.059 - DIS