Data/hora: 23/09/2024 23:43:38

Biblioteca(s): 	Embrapa Agricultura Digital.	
Tipo da produção científica: 	Artigo em Periódico Indexado	
Circulação/Nível: 	C - 0	
Data corrente: 	07/10/2010	
Data da última atualização: 	23/05/2011	
Autoria: 	ROMANI, L. A. S.; ÁVILA, A. M. H.; ZULLO JÚNIOR, J.; TRAINA JÚNIOR, C.; TRAINA, A. J. M.	
Afiliação: 	LUCIANA ALVIM SANTOS ROMANI, CNPTIA; ANA MARIA H. ÁVILA, CEPAGRI/UNICAMP; JURANDIR ZULLO JÚNIOR, CEPAGRI/UNICAMP; CAETANO TRAINA JÚNIOR, ICMC/USP; AGMA J. M. TRAINA, ICMC/USP.	
Título: 	Mining relevant and extreme patterns on climate time series with CLIPSMiner.	
Ano de publicação: 	2010	
Fonte/Imprenta: 	Journal of Information and Data Management, Belo Horizonte, v. 1, n. 2, p. 245-260. June 2010.	
Idioma: 	Inglês	
Conteúdo: 	One of the most important challenges for the researchers in the 21st Century is related to global heating and climate change that can have as consequence the intensification of natural hazards. Another problem of changes in the Earth's climate is its impact in the agriculture production. In this scenario, application of statistical models as well as development of new methods become very important to aid in the analyses of climate from ground-based stations and outputs of forecasting models. Additionally, remote sensing images have been used to improve the monitoring of crop yields. In this context we propose a new technique to identify extreme values in climate time series and to correlate climate and remote sensing data in order to improve agricultural monitoring. Accordingly, this paper presents a new unsupervised algorithm, called CLIPSMiner (CLImate PatternS Miner) that works on multiple time series of continuous data, identifying relevant patterns or extreme ones according to a relevance factor, which can be tuned by the user. Results show that CLIPSMiner detects, as expected, patterns that are known in climatology, indicating the correctness and feasibility of the proposed algorithm. Moreover, patterns detected using the highest relevance factor is coincident with extreme phenomena. Furthermore, series correlations detected by the algorithm show a relation between agroclimatic and vegetation indices, which confirms the agrometeorologists' expectations.	
Palavras-Chave: 	Algoritmo CLIPSMiner; Data mining; Mineração de dados.	
Thesagro: 	Sensoriamento Remoto.	
Thesaurus Nal: 	Climate change; Remote sensing.	
Categoria do assunto: 	--	
URL: 	http://ainfo.cnptia.embrapa.br/digital/bitstream/item/23218/1/39-220-2-PB.pdf	
Registro original: 	Embrapa Agricultura Digital (CNPTIA)	
Exemplares: 	CNPTIA - (15165-1) - UPC - AP - DD -  -  -  - DIS