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Registro Completo |
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Biblioteca(s): |
Embrapa Gado de Leite. |
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Data corrente: |
10/11/1992 |
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Data da última atualização: |
10/11/1992 |
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Autoria: |
CAMPSO, O. F. de; LIZIEIRE, R. S. |
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Afiliação: |
CNPGL. |
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Título: |
Vacas leiteiras no periodo seco. |
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Ano de publicação: |
1991 |
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Fonte/Imprenta: |
O Produtor de Leite, Rio de Janeiro, v.20, n.125, mar./abr., p.30-34, 1991. |
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Idioma: |
Português |
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Palavras-Chave: |
Bovino de leite; Pre-parto. |
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Thesagro: |
Manejo; Produção. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 00457naa a2200169 a 4500
001 1590005
005 1992-11-10
008 1991 bl uuuu u00u1 u #d
100 1 $aCAMPSO, O. F. de
245 $aVacas leiteiras no periodo seco.
260 $c1991
650 $aManejo
650 $aProdução
653 $aBovino de leite
653 $aPre-parto
700 1 $aLIZIEIRE, R. S.
773 $tO Produtor de Leite, Rio de Janeiro$gv.20, n.125, mar./abr., p.30-34, 1991.
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Embrapa Gado de Leite (CNPGL) |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
11/01/2013 |
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Data da última atualização: |
08/01/2020 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
CANO, M. D.; SANTOS, M. T. P.; AVILA, A. M. H. de; ROMANI, L. A. S.; TRAINA, A. J. M.; RIBEIRO, M. X. |
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Afiliação: |
MARCOS DANIEL CANO, UFSCar; MARILDE TEREZINHA PRADO SANTOS, UFSCar; ANA MARIA H. DE AVILA, Cepagri/Unicamp; LUCIANA ALVIM SANTOS ROMANI, CNPTIA; AGMA J. M. TRAINA, ICMC/USP; MARCELA XAVIER RIBEIRO, UFSCar. |
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Título: |
SART: a new association rule method for mining sequential patterns in time series of climate data. |
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Ano de publicação: |
2012 |
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Fonte/Imprenta: |
In: INTERNATIONAL CONFERENCE, 12., 2012, Salvador de Bahia. Computational science and its applications: proceedings. Berlin: Springer, 2012. |
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Páginas: |
p. 743-757. |
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Série: |
(Lecture notes in computer science, 7335). |
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Idioma: |
Inglês |
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Notas: |
Part III. ICCSA 2012. Editores: Beniamino Murgante, Osvaldo Gervasi, Sanjay Misra, Nadia, Nejah, Ana Maria A. C. Rocha, David Taniar, Bernardy I, Apduhan. |
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Conteúdo: |
Abstract. Technological advancement has enabled improvements in the technology of sensors and satellites used to gather climate data. The time series mining is an important tool to analyze the huge quantity of climate data. Here, we propose the Sequential Association Rules from Time series - SART method to mine association rules in time series that keeps the information of time between related events through an overlapped sliding-window approach. Also the proposed method mines association rules, while the previous ones produce frequent sequences, adding the semantic information of confidence, which was not previously defined by sequential patterns. Experiments were conducted with real data collected from climate sensors. The results showed that the proposed method increases the number of mined patterns when compared with the traditional sequential mining, revealing related events that occur over time. Also, the method adds the semantic information related to the confidence and time to the mined patterns. |
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Palavras-Chave: |
Data mining; Mineração de dados; Séries temporais. |
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Thesaurus NAL: |
Time series analysis. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02039nam a2200253 a 4500
001 1944713
005 2020-01-08
008 2012 bl uuuu u00u1 u #d
100 1 $aCANO, M. D.
245 $aSART$ba new association rule method for mining sequential patterns in time series of climate data.$h[electronic resource]
260 $aIn: INTERNATIONAL CONFERENCE, 12., 2012, Salvador de Bahia. Computational science and its applications: proceedings. Berlin: Springer$c2012
300 $ap. 743-757.
490 $a(Lecture notes in computer science, 7335).
500 $aPart III. ICCSA 2012. Editores: Beniamino Murgante, Osvaldo Gervasi, Sanjay Misra, Nadia, Nejah, Ana Maria A. C. Rocha, David Taniar, Bernardy I, Apduhan.
520 $aAbstract. Technological advancement has enabled improvements in the technology of sensors and satellites used to gather climate data. The time series mining is an important tool to analyze the huge quantity of climate data. Here, we propose the Sequential Association Rules from Time series - SART method to mine association rules in time series that keeps the information of time between related events through an overlapped sliding-window approach. Also the proposed method mines association rules, while the previous ones produce frequent sequences, adding the semantic information of confidence, which was not previously defined by sequential patterns. Experiments were conducted with real data collected from climate sensors. The results showed that the proposed method increases the number of mined patterns when compared with the traditional sequential mining, revealing related events that occur over time. Also, the method adds the semantic information related to the confidence and time to the mined patterns.
650 $aTime series analysis
653 $aData mining
653 $aMineração de dados
653 $aSéries temporais
700 1 $aSANTOS, M. T. P.
700 1 $aAVILA, A. M. H. de
700 1 $aROMANI, L. A. S.
700 1 $aTRAINA, A. J. M.
700 1 $aRIBEIRO, M. X.
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