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dc.contributor.authorROMANI, L. A. S.pt_BR
dc.contributor.authorÁVILA, A. M. H.pt_BR
dc.contributor.authorZULLO JÚNIOR, J.pt_BR
dc.contributor.authorTRAINA JÚNIOR, C.pt_BR
dc.contributor.authorTRAINA, A. J. M.pt_BR
dc.date.accessioned2011-04-10T11:11:11Zpt_BR
dc.date.available2011-04-10T11:11:11Zpt_BR
dc.date.created2010-10-07pt_BR
dc.date.issued2010pt_BR
dc.identifier.citationJournal of Information and Data Management, Belo Horizonte, v. 1, n. 2, p. 245-260. June 2010.pt_BR
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/863850pt_BR
dc.descriptionOne 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.pt_BR
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectMineração de dadospt_BR
dc.subjectAlgoritmo CLIPSMinerpt_BR
dc.subjectData miningpt_BR
dc.titleMining relevant and extreme patterns on climate time series with CLIPSMiner.pt_BR
dc.typeArtigo de periódicopt_BR
dc.date.updated2011-05-23T11:11:11Zpt_BR
dc.subject.thesagroSensoriamento Remotopt_BR
dc.subject.nalthesaurusClimate changept_BR
dc.subject.nalthesaurusRemote sensingpt_BR
riaa.ainfo.id863850pt_BR
riaa.ainfo.lastupdate2011-05-23pt_BR
dc.contributor.institutionLUCIANA 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.pt_BR
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