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Mandoni R.j.a. - One of the best experts on this subject based on the ideXlab platform.

  • Analyze The Soil Attributes And Sugarcane Yield Culture With The Use Of Geostatistics And Decision Trees [análise Dos Atributos Do Solo E Da Produtividade Da Cultura De Cana-de-açúcar Com O Uso Da Geoestatística E árvore De Decisão]
    2015
    Co-Authors: De Souza Z.m., Cerri D.g.p., Colet M.j., Rodrigues L.h.a., Magalhaes P.s.g., Mandoni R.j.a.
    Abstract:

    One of the challenges of precision agriculture is to offer subsidies for the definition of management units for posterior interventions. Therefore, the objective of this work was to evaluate soil chemical attributes and sugarcane yield with the use of geostatistics and data mining by decision tree induction. Sugarcane yield was mapped in a 23ha field, applying the cell criterion, by using a yield monitor that allowed the elaboration of a digital map representing the surface of production of the studied area. To determine the soil attributes, soil samples were collected at the beginning of the harvest in 2006/2007 using a regular grid of 50 x 50m, in the depths of 0.0-0.2m and 0.2-0.4m. Soil attributes and sugarcane yield data were analyzed by using geostatistics techniques and were classified into three yield levels for the elaboration of the decision tree. The decision tree was induced in the software SAS Enterprise Miner, using an algorithm based on entropy reduction. Altitude and potassium presented the highest values of correlation with sugarcane yield. The induction of decision trees showed that the altitude is the variable with the greatest potential to interpret the sugarcane yield maps, then assisting in precision agriculture and, revealing an adjusted tool for the study of management definition zones in area cropped with sugarcane

  • Analyze The Soil Attributes And Sugarcane Yield Culture With The Use Of Geostatistics And Decision Trees [análise Dos Atributos Do Solo E Da Produtividade Da Cultura De Cana-de-açúcar Com O Uso Da Geoestatística E árvore De Decisão]
    2015
    Co-Authors: De Souza Z.m., Cerri D.g.p., Colet M.j., Rodrigues L.h.a., Magalhaes P.s.g., Mandoni R.j.a.
    Abstract:

    One of the challenges of precision agriculture is to offer subsidies for the definition of management units for posterior interventions. Therefore, the objective of this work was to evaluate soil chemical attributes and sugarcane yield with the use of geostatistics and data mining by decision tree induction. Sugarcane yield was mapped in a 23ha field, applying the cell criterion, by using a yield monitor that allowed the elaboration of a digital map representing the surface of production of the studied area. To determine the soil attributes, soil samples were collected at the beginning of the harvest in 2006/2007 using a regular grid of 50 x 50m, in the depths of 0.0-0.2m and 0.2-0.4m. Soil attributes and sugarcane yield data were analyzed by using geostatistics techniques and were classified into three yield levels for the elaboration of the decision tree. The decision tree was induced in the software SAS Enterprise Miner, using an algorithm based on entropy reduction. Altitude and potassium presented the highest values of correlation with sugarcane yield. The induction of decision trees showed that the altitude is the variable with the greatest potential to interpret the sugarcane yield maps, then assisting in precision agriculture and, revealing an adjusted tool for the study of management definition zones in area cropped with sugarcane.404840847Burrough, P.A., The state of the art in pedometrics (1994) Geoderma, 62 (1-3), pp. 311-326. , http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V67-48B0MYK-88&_user=972058&_rdoc=1&_fmt=&_orig=search&_sort=d&_docanchor=&view=c&_acct=C000049648&_version=1&_urlVersion=0&_userid=972058&md5=a9197027531de4960615e2e39b8d3ef7, Amsterdam, Disponível em, Acesso em: 22 de nov. 2008. doi: 10.1016/0016-7061(94)90043-4Cambardella, C.A., Field-scale variability of soil properties in Central Iowa (1994) Soil Science Society of American Journal, 58 (5), pp. 1501-1511. , MadisonCorá, J.E., Variabilidade espacial de atributos do solo para adoção do sistema de agricultura de precisão na cultura de cana-de-açúcar (2004) Revista Brasileira De Ciência Do Solo, 28 (6), pp. 1013-1021. , http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832004000600010&lng=en&nrm=iso&tlng=pt, Viçosa, Disponível em, Acesso em: 22 out. 2008. doi: 10.1590/S0100-06832004000600010de la Rosa, D., Expert evaluation system for assessing field vulnerability to agrochemical compounds in Mediterranean regions (1993) Journal of Agricultural Engineering Research, 56 (2), pp. 153-164. , http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WH1-45P12Y2-S&_user=972058&_rdoc=1&_fmt=&_orig=search&_sort=d&_docanchor=&view=c&_acct=C000049648&_version=1&_urlVersion=0&_userid=972058&md5=30e198bd86ceed800a9ca256312ce655, London, Disponível em, Acesso em: 20 de nov. 2008. doi: 10.1006/jaer.1993.1068(1997) Manual De Métodos De Análise De Solo, p. 212. , EMPRESA BRASILEIRA DE PESQUISA AGROPECUÁRIA - EMBRAPA, 2.ed. Rio de Janeiro: Ministério da Agricultura e do AbastecimentoGrego, C.R., Vieira, S.R., Variabilidade espacial de propriedades físicas do solo em uma parcela experimental (2005) Revista Brasileira De Ciência Do Solo, 29 (2), pp. 169-177. , http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832005000200002&lng=pt&nrm=iso&tlng=pt, Viçosa, Disponível em, Acesso em: 19 de out. 2008. doi: 10.1590/S0100-06832005000200002Han, J., Kamber, M., (2001) Data Mining: Concepts and Techniques, p. 550. , San Francisco: Morgan Kaufmann/CAIsaaks, E.H., Srivastava, R.M., (1989) An Introduction to Applied Geoestatistics, p. 561. , New York: Oxford UniversityKitamura, A.E., Relação entre a variabilidade espacial das frações granulométricas do solo e a produtividade do feijoeiro sob plantio direto (2007) Revista Brasileira De Ciência Do Solo, 31 (2), pp. 361-369. , http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832007000200018&lng=pt&nrm=iso&tlng=pt, Viçosa, Disponível em, Acesso em: 08 de nov. 2008. doi: 10.1590/S0100-06832007000200018Kravchenko, A.N., Bullock, D.G., Correlation of corn and soybean yield with topography and soil properties (2000) Agronomy Journal, 75 (1), pp. 75-83. , http://agron.scijournals.org/cgi/content/full/92/1/75, Madison, Disponível em, Acesso em: 13 de out. 2007Mata, J.V.D., Relação entre produtividade e resistência à penetração em área irrigada por pivô central, sob dois sistemas de preparo (1999) Acta Scientiarum, 21 (3), pp. 519-525. , http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/4264/2942, Maringá, Disponível em, Acesso em: 20 de fev. 2008Megda, M.M., Correlação linear e espacial entre a produtividade de feijão e a porosidade de um Latossolo Vermelho de Selvíria (MS) (2008) Revista Brasileira De Ciência Do Solo, 32 (2), pp. 781-788. , http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832008000200032&lng=pt&nrm=iso&tlng=pt, Viçosa, Disponível em, Acesso em: 18 de Nov. 2007, doi: 10.1590/S0100-06832008000200032Montezano, Z.F., Variabilidade de nutrientes em plantas de milho cultivado em talhão manejado homogeneamente (2008) Bragantia, Campinas, 67 (4), pp. 969-976. , http://www.scielo.br/scielo.php?pid=S0006-87052008000400020&script=sci_arttext&tlng=target=_blank, Disponível em, Acesso em: 01 de nov. 2007. doi: 10.1590/S0006-87052008000400020Quinlan, J.R., (1993) C4.5: Programs For Machine Learning, pp. 234-278. , San Francisco: Morgan Kaufmann/CARachid Jr., A., Variabilidade espacial e temporal de atributos químicos do solo e da produtividade da soja num sistema de agricultura de precisão (2006) Engenharia Na Agricultura, 14 (3), pp. 156-169. , http://www.seer.ufv.br/seer/index.php/reveng/article/viewFile/116/51, Viçosa, Disponível em, Acesso em: 22 de out. 2008(2001) Análise Química Para Avaliação Da Fertilidade De Solos Tropicais, p. 285. , RAIJ, B. van et al. (Ed), Campinas: Instituto AgronômicoRobertson, G.P., (1998) GS +: Geostatistics For the Environmental Sciences (version 5.1 For Windows), p. 152. , Plainwell: Gamma Design SoffwareRosa Filho, G., Variabilidade da produtividade da soja em função de atributos físicos de um Latossolo Vermelho distroférrico sob plantio direto (2009) Revista Brasileira De Ciência Do Solo, 33 (2), pp. 275-283. , http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832009000200006&lng=pt&nrm=iso&tlng=pt, Viçosa, Disponível em, Acesso em: 25 de nov. 2008. doi: 10.1590/S0100-06832009000200006Schlotzhaver, S.D., Littell, R.C., (1997) SAS: System For Elementary Statistical Analysis, p. 905. , 2.ed. Cary: SASSouza, C.K., Influência do relevo na variação anisotrópica dos atributos químicos e granulométricos de uma latossolo em Jaboticabal-SP (2003) Engenharia Agrícola, Jaboticabal, 23 (3), pp. 486-495Souza, Z.M., Variabilidade espacial do pH, Ca, Mg e V% do solo em diferentes formas do relevo sob cultivo de cana-deaçúcar (2004) Ciência Rural, 34 (6), pp. 1763-1771. , http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782004000600015&lng=pt&nrm=iso&tlng=pt, Santa Maria, Disponível em, Acesso em: 01 de dez. 2007. doi: 10.1590/S0103-84782004000600015Vanni, S.M., (1998) Modelos De Regressão: Estatística Aplicada, p. 177. , São Paulo: Legmar Informática & EditoraVieira, S.R., Geoestatística em estudos de variabilidade espacial do solo (2000) Tópicos Em Ciência Do Solo, 1, pp. 1-53. , NOVAIS, R.F. et al. (Eds), Viçosa: Sociedade Brasileira de Ciência do SoloVieira, S.R., Molin, J.P., Spatial variability of soil fertility for precision agriculture (2001) Proceedings... Montpellier: Agro Montpellier, 1 (3), pp. 491-496. , EUROPEAN CONFERENCE ON PRECISION AGRICULTURE, 3., 2001, MontpellierWarrick, A.W., Nielsen, D.R., Spatial variability of soil physical properties in the field (1980) Applications of Soil Physics, pp. 319-344. , HILLEL, D. (Ed.), New York: Academic, Cap.2Webster, R., Statistics to support soil research and their presentation (2001) European Journal of Soil Science, 52 (2), pp. 331-340. , http://www3.interscience.wiley.com/cgi-bin/fulltext/122371407/HTMLSTART, Oxford, Disponível em, Acesso em: 12 de out. 2007Yanai, J., Geostatistical analysis of soil chemical properties and rice yield in a paddy field and application to the analysis of yield-determining factors (2001) Soil Science and Plant Nutrition, 47 (2), pp. 291-301. , TokyoYang, C., Use of hyperspectral imagery for identification of different fertilization methods with decision-tree technology (2002) Biosystems Engineering, 83 (3), pp. 291-298. , Amsterda

De Souza Z.m. - One of the best experts on this subject based on the ideXlab platform.

  • Analyze The Soil Attributes And Sugarcane Yield Culture With The Use Of Geostatistics And Decision Trees [análise Dos Atributos Do Solo E Da Produtividade Da Cultura De Cana-de-açúcar Com O Uso Da Geoestatística E árvore De Decisão]
    2015
    Co-Authors: De Souza Z.m., Cerri D.g.p., Colet M.j., Rodrigues L.h.a., Magalhaes P.s.g., Mandoni R.j.a.
    Abstract:

    One of the challenges of precision agriculture is to offer subsidies for the definition of management units for posterior interventions. Therefore, the objective of this work was to evaluate soil chemical attributes and sugarcane yield with the use of geostatistics and data mining by decision tree induction. Sugarcane yield was mapped in a 23ha field, applying the cell criterion, by using a yield monitor that allowed the elaboration of a digital map representing the surface of production of the studied area. To determine the soil attributes, soil samples were collected at the beginning of the harvest in 2006/2007 using a regular grid of 50 x 50m, in the depths of 0.0-0.2m and 0.2-0.4m. Soil attributes and sugarcane yield data were analyzed by using geostatistics techniques and were classified into three yield levels for the elaboration of the decision tree. The decision tree was induced in the software SAS Enterprise Miner, using an algorithm based on entropy reduction. Altitude and potassium presented the highest values of correlation with sugarcane yield. The induction of decision trees showed that the altitude is the variable with the greatest potential to interpret the sugarcane yield maps, then assisting in precision agriculture and, revealing an adjusted tool for the study of management definition zones in area cropped with sugarcane

  • Analyze The Soil Attributes And Sugarcane Yield Culture With The Use Of Geostatistics And Decision Trees [análise Dos Atributos Do Solo E Da Produtividade Da Cultura De Cana-de-açúcar Com O Uso Da Geoestatística E árvore De Decisão]
    2015
    Co-Authors: De Souza Z.m., Cerri D.g.p., Colet M.j., Rodrigues L.h.a., Magalhaes P.s.g., Mandoni R.j.a.
    Abstract:

    One of the challenges of precision agriculture is to offer subsidies for the definition of management units for posterior interventions. Therefore, the objective of this work was to evaluate soil chemical attributes and sugarcane yield with the use of geostatistics and data mining by decision tree induction. Sugarcane yield was mapped in a 23ha field, applying the cell criterion, by using a yield monitor that allowed the elaboration of a digital map representing the surface of production of the studied area. To determine the soil attributes, soil samples were collected at the beginning of the harvest in 2006/2007 using a regular grid of 50 x 50m, in the depths of 0.0-0.2m and 0.2-0.4m. Soil attributes and sugarcane yield data were analyzed by using geostatistics techniques and were classified into three yield levels for the elaboration of the decision tree. The decision tree was induced in the software SAS Enterprise Miner, using an algorithm based on entropy reduction. Altitude and potassium presented the highest values of correlation with sugarcane yield. The induction of decision trees showed that the altitude is the variable with the greatest potential to interpret the sugarcane yield maps, then assisting in precision agriculture and, revealing an adjusted tool for the study of management definition zones in area cropped with sugarcane.404840847Burrough, P.A., The state of the art in pedometrics (1994) Geoderma, 62 (1-3), pp. 311-326. , http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V67-48B0MYK-88&_user=972058&_rdoc=1&_fmt=&_orig=search&_sort=d&_docanchor=&view=c&_acct=C000049648&_version=1&_urlVersion=0&_userid=972058&md5=a9197027531de4960615e2e39b8d3ef7, Amsterdam, Disponível em, Acesso em: 22 de nov. 2008. doi: 10.1016/0016-7061(94)90043-4Cambardella, C.A., Field-scale variability of soil properties in Central Iowa (1994) Soil Science Society of American Journal, 58 (5), pp. 1501-1511. , MadisonCorá, J.E., Variabilidade espacial de atributos do solo para adoção do sistema de agricultura de precisão na cultura de cana-de-açúcar (2004) Revista Brasileira De Ciência Do Solo, 28 (6), pp. 1013-1021. , http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832004000600010&lng=en&nrm=iso&tlng=pt, Viçosa, Disponível em, Acesso em: 22 out. 2008. doi: 10.1590/S0100-06832004000600010de la Rosa, D., Expert evaluation system for assessing field vulnerability to agrochemical compounds in Mediterranean regions (1993) Journal of Agricultural Engineering Research, 56 (2), pp. 153-164. , http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WH1-45P12Y2-S&_user=972058&_rdoc=1&_fmt=&_orig=search&_sort=d&_docanchor=&view=c&_acct=C000049648&_version=1&_urlVersion=0&_userid=972058&md5=30e198bd86ceed800a9ca256312ce655, London, Disponível em, Acesso em: 20 de nov. 2008. doi: 10.1006/jaer.1993.1068(1997) Manual De Métodos De Análise De Solo, p. 212. , EMPRESA BRASILEIRA DE PESQUISA AGROPECUÁRIA - EMBRAPA, 2.ed. Rio de Janeiro: Ministério da Agricultura e do AbastecimentoGrego, C.R., Vieira, S.R., Variabilidade espacial de propriedades físicas do solo em uma parcela experimental (2005) Revista Brasileira De Ciência Do Solo, 29 (2), pp. 169-177. , http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832005000200002&lng=pt&nrm=iso&tlng=pt, Viçosa, Disponível em, Acesso em: 19 de out. 2008. doi: 10.1590/S0100-06832005000200002Han, J., Kamber, M., (2001) Data Mining: Concepts and Techniques, p. 550. , San Francisco: Morgan Kaufmann/CAIsaaks, E.H., Srivastava, R.M., (1989) An Introduction to Applied Geoestatistics, p. 561. , New York: Oxford UniversityKitamura, A.E., Relação entre a variabilidade espacial das frações granulométricas do solo e a produtividade do feijoeiro sob plantio direto (2007) Revista Brasileira De Ciência Do Solo, 31 (2), pp. 361-369. , http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832007000200018&lng=pt&nrm=iso&tlng=pt, Viçosa, Disponível em, Acesso em: 08 de nov. 2008. doi: 10.1590/S0100-06832007000200018Kravchenko, A.N., Bullock, D.G., Correlation of corn and soybean yield with topography and soil properties (2000) Agronomy Journal, 75 (1), pp. 75-83. , http://agron.scijournals.org/cgi/content/full/92/1/75, Madison, Disponível em, Acesso em: 13 de out. 2007Mata, J.V.D., Relação entre produtividade e resistência à penetração em área irrigada por pivô central, sob dois sistemas de preparo (1999) Acta Scientiarum, 21 (3), pp. 519-525. , http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/4264/2942, Maringá, Disponível em, Acesso em: 20 de fev. 2008Megda, M.M., Correlação linear e espacial entre a produtividade de feijão e a porosidade de um Latossolo Vermelho de Selvíria (MS) (2008) Revista Brasileira De Ciência Do Solo, 32 (2), pp. 781-788. , http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832008000200032&lng=pt&nrm=iso&tlng=pt, Viçosa, Disponível em, Acesso em: 18 de Nov. 2007, doi: 10.1590/S0100-06832008000200032Montezano, Z.F., Variabilidade de nutrientes em plantas de milho cultivado em talhão manejado homogeneamente (2008) Bragantia, Campinas, 67 (4), pp. 969-976. , http://www.scielo.br/scielo.php?pid=S0006-87052008000400020&script=sci_arttext&tlng=target=_blank, Disponível em, Acesso em: 01 de nov. 2007. doi: 10.1590/S0006-87052008000400020Quinlan, J.R., (1993) C4.5: Programs For Machine Learning, pp. 234-278. , San Francisco: Morgan Kaufmann/CARachid Jr., A., Variabilidade espacial e temporal de atributos químicos do solo e da produtividade da soja num sistema de agricultura de precisão (2006) Engenharia Na Agricultura, 14 (3), pp. 156-169. , http://www.seer.ufv.br/seer/index.php/reveng/article/viewFile/116/51, Viçosa, Disponível em, Acesso em: 22 de out. 2008(2001) Análise Química Para Avaliação Da Fertilidade De Solos Tropicais, p. 285. , RAIJ, B. van et al. (Ed), Campinas: Instituto AgronômicoRobertson, G.P., (1998) GS +: Geostatistics For the Environmental Sciences (version 5.1 For Windows), p. 152. , Plainwell: Gamma Design SoffwareRosa Filho, G., Variabilidade da produtividade da soja em função de atributos físicos de um Latossolo Vermelho distroférrico sob plantio direto (2009) Revista Brasileira De Ciência Do Solo, 33 (2), pp. 275-283. , http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832009000200006&lng=pt&nrm=iso&tlng=pt, Viçosa, Disponível em, Acesso em: 25 de nov. 2008. doi: 10.1590/S0100-06832009000200006Schlotzhaver, S.D., Littell, R.C., (1997) SAS: System For Elementary Statistical Analysis, p. 905. , 2.ed. Cary: SASSouza, C.K., Influência do relevo na variação anisotrópica dos atributos químicos e granulométricos de uma latossolo em Jaboticabal-SP (2003) Engenharia Agrícola, Jaboticabal, 23 (3), pp. 486-495Souza, Z.M., Variabilidade espacial do pH, Ca, Mg e V% do solo em diferentes formas do relevo sob cultivo de cana-deaçúcar (2004) Ciência Rural, 34 (6), pp. 1763-1771. , http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782004000600015&lng=pt&nrm=iso&tlng=pt, Santa Maria, Disponível em, Acesso em: 01 de dez. 2007. doi: 10.1590/S0103-84782004000600015Vanni, S.M., (1998) Modelos De Regressão: Estatística Aplicada, p. 177. , São Paulo: Legmar Informática & EditoraVieira, S.R., Geoestatística em estudos de variabilidade espacial do solo (2000) Tópicos Em Ciência Do Solo, 1, pp. 1-53. , NOVAIS, R.F. et al. (Eds), Viçosa: Sociedade Brasileira de Ciência do SoloVieira, S.R., Molin, J.P., Spatial variability of soil fertility for precision agriculture (2001) Proceedings... Montpellier: Agro Montpellier, 1 (3), pp. 491-496. , EUROPEAN CONFERENCE ON PRECISION AGRICULTURE, 3., 2001, MontpellierWarrick, A.W., Nielsen, D.R., Spatial variability of soil physical properties in the field (1980) Applications of Soil Physics, pp. 319-344. , HILLEL, D. (Ed.), New York: Academic, Cap.2Webster, R., Statistics to support soil research and their presentation (2001) European Journal of Soil Science, 52 (2), pp. 331-340. , http://www3.interscience.wiley.com/cgi-bin/fulltext/122371407/HTMLSTART, Oxford, Disponível em, Acesso em: 12 de out. 2007Yanai, J., Geostatistical analysis of soil chemical properties and rice yield in a paddy field and application to the analysis of yield-determining factors (2001) Soil Science and Plant Nutrition, 47 (2), pp. 291-301. , TokyoYang, C., Use of hyperspectral imagery for identification of different fertilization methods with decision-tree technology (2002) Biosystems Engineering, 83 (3), pp. 291-298. , Amsterda

Sascha Schubert - One of the best experts on this subject based on the ideXlab platform.

  • it s about time discrete time survival analysis using sas Enterprise Miner
    2012
    Co-Authors: Sascha Schubert, Taiyeong Lee
    Abstract:

    The new survival analysis algorithm in SAS Enterprise Miner 7.1 provides analysts with an alternate approach to modeling the probability of customer behavior events. Traditional binary classification approaches provide a snapshot view of event propensities, while survival analysis can generate a time based function of event probabilities. This time-based view can help organizations to optimize their customer strategies by gaining a more complete picture of customer event likelihoods. This paper briefly explains the theory of survival analysis and provides an introduction to its implementation in SAS Enterprise Miner. An example illustrates the usage of this analytical algorithm using a customer churn data set.

  • time series data mining with sas Enterprise Miner
    2011
    Co-Authors: Sascha Schubert, Taiyeong Lee
    Abstract:

    Traditionally, data mining and time series analysis have been seen as separate approaches to analyzing Enterprise data. However, much of the data generated by business processes is time-stamped. Time series data mining is a marriage of forecasting and traditional data mining techniques that uses time dimensions and predictive analytics to to make better business decisions. SAS has developed a collection of techniques that will be integrated into SAS ® Enterprise Miner 7.1 ™ . This paper introduces these new time series data mining techniques.

  • paper 123 2010 the power of the group processing facility in sas Enterprise Miner
    2010
    Co-Authors: Sascha Schubert
    Abstract:

    The group processing facility in SAS ® Enterprise Miner™ is useful when your data can be segmented or grouped, and you want to process the grouped data in different ways. It uses BY-group processing to process observations from one or more data sources that are grouped or ordered by values of one or more common variables. For example, model projects that leverage the same input data space and multiple target variables can be easily and efficiently managed and processed by using the group processing facility.

  • paper 145 2008 tailoring the use of sas Enterprise Miner
    2008
    Co-Authors: Sascha Schubert
    Abstract:

    A growing number of SAS users with different goals and skill levels need access to data mining functionality. The new generation of SAS ® Enterprise Miner™ 5 and the SAS stored process facility provide an easy way to tailor data mining functionality to the user's needs. The flexible architecture of SAS Enterprise Miner and the integration of Enterprise Miner into the SAS Enterprise Intelligence architecture allow the product users to create data mining projects for interactive or batch execution and share projects with other users. The software’s Extension facility allows users to build specific functions that are fully integrated into the Enterprise Miner workbench. Based on the integrated batch processing capabilities, model training and model scoring code can be easily extracted from SAS Enterprise Miner and integrated into the SAS Enterprise Intelligence Platform using the SAS stored process facility. For users of the SAS Add-In for Microsoft Office, customized data mining interfaces can be integrated into their favorite Microsoft applications.

Goutam Chakraborty - One of the best experts on this subject based on the ideXlab platform.

  • donor sentiment and characteristic analysis using sas Enterprise Miner and sas sentiment analysis studio
    2015
    Co-Authors: Ramcharan Kakarla, Goutam Chakraborty
    Abstract:

    It has always been a million-dollar question, “What inhibits a donor to donate?” Many successful universities have deep roots in annual giving. We know sentiment is a key factor in drawing attention to engage donors. This paper is a summary of findings about donor behaviors using textual analysis combined with the power of predictive modeling. In addition to identifying the characteristics of general donors, the paper focuses on identifying the characteristics of a first-time donor. It distinguishes the features of the first-time donor from the general donor pattern. A data set containing 247,000 records was obtained from a University Foundation alumni database, Facebook, and Twitter. Solicitation content such as email subject lines sent to the prospect base was considered. Time-dependent data and time-independent data were categorized to make unbiased predictions about the first-time donor. The predictive models use inputs such as age, educational records, scholarships, events, student memberships, and solicitation methods. Models such as decision trees, Dmine regression, and neural networks were built to predict the prospects. SAS® Sentiment Analysis Studio and SAS® Enterprise Miner™ were used to analyze the sentiment.

  • internet gambling behavioral markers using the power of sas Enterprise Miner 12 1 to predict high risk internet gamblers
    2014
    Co-Authors: Sai Vijay, Kishore Movva, Vandana Reddy, Goutam Chakraborty
    Abstract:

    Using 4,056 subscribers’ data about actual gambling behavior over the Internet, we developed behavioral markers which can be used to predict the level of risk that a subscriber is prone to gambling addiction. SAS® Enterprise Miner™ 12.1 is used to build a set of models to predict which subscriber is likely to become a high-risk internet gambler. The data contains 114 variables such as “first active date” and “first active product used” on the website as well as the characteristics of the game such as fixed odds, poker, casino, games, etc. Other measures of a subscriber’s data such as money put at stake and what odds are being bet are also included in the analysis. These variables provide a comprehensive view of a subscriber’s behavior while gambling over the website. The target variable is modeled as a binary variable, 0 indicating a risky gambler and 1 indicating a controlled gambler. The model comparison algorithm of SAS® Enterprise Miner™ 12.1 is used to determine the best model. The stepwise regression performs the best among a set of 25 models which are run using over 100 permutations of each model. The stepwise regression model predicts a high-risk Internet gambler with an accuracy of 69.63% with variables capturing individual’s behavioral patterns.

  • application of time series clustering using sas Enterprise Miner tm for a retail chain sas global forum 2012
    2012
    Co-Authors: Karthik Nakkeeran, Satish Garla, Goutam Chakraborty
    Abstract:

    Much of the data that are generated in the operational side of a business have a built-in time dimension. One of the challenges of doing data mining using such time-series data is the complexity of handling a large number of time series. Time series clustering provides a way to reduce the complexity by categorizing large number of time series into a smaller subset such that series within each subset are relatively homogenous but series between subsets are heterogeneous.

  • the increased utilization of analytics and how academic programs are responding to job market demands by leveraging predictive modeling with sas Enterprise Miner certification
    2009
    Co-Authors: Julie Petlick, Goutam Chakraborty
    Abstract:

    According to Gartner, more than 90% of global 2000 companies have incorporated, or plan to incorporate, analytics into their business applications. This increased utilization of analytics will likely correspond to an increased demand for career professionals with strong analytic skills. This paper examines how academic programs are responding to this need by incorporating the Predictive Modeling with SAS ® Enterprise Miner™ certification exam into their existing curricula.

Magalhaes P.s.g. - One of the best experts on this subject based on the ideXlab platform.

  • Analyze The Soil Attributes And Sugarcane Yield Culture With The Use Of Geostatistics And Decision Trees [análise Dos Atributos Do Solo E Da Produtividade Da Cultura De Cana-de-açúcar Com O Uso Da Geoestatística E árvore De Decisão]
    2015
    Co-Authors: De Souza Z.m., Cerri D.g.p., Colet M.j., Rodrigues L.h.a., Magalhaes P.s.g., Mandoni R.j.a.
    Abstract:

    One of the challenges of precision agriculture is to offer subsidies for the definition of management units for posterior interventions. Therefore, the objective of this work was to evaluate soil chemical attributes and sugarcane yield with the use of geostatistics and data mining by decision tree induction. Sugarcane yield was mapped in a 23ha field, applying the cell criterion, by using a yield monitor that allowed the elaboration of a digital map representing the surface of production of the studied area. To determine the soil attributes, soil samples were collected at the beginning of the harvest in 2006/2007 using a regular grid of 50 x 50m, in the depths of 0.0-0.2m and 0.2-0.4m. Soil attributes and sugarcane yield data were analyzed by using geostatistics techniques and were classified into three yield levels for the elaboration of the decision tree. The decision tree was induced in the software SAS Enterprise Miner, using an algorithm based on entropy reduction. Altitude and potassium presented the highest values of correlation with sugarcane yield. The induction of decision trees showed that the altitude is the variable with the greatest potential to interpret the sugarcane yield maps, then assisting in precision agriculture and, revealing an adjusted tool for the study of management definition zones in area cropped with sugarcane

  • Analyze The Soil Attributes And Sugarcane Yield Culture With The Use Of Geostatistics And Decision Trees [análise Dos Atributos Do Solo E Da Produtividade Da Cultura De Cana-de-açúcar Com O Uso Da Geoestatística E árvore De Decisão]
    2015
    Co-Authors: De Souza Z.m., Cerri D.g.p., Colet M.j., Rodrigues L.h.a., Magalhaes P.s.g., Mandoni R.j.a.
    Abstract:

    One of the challenges of precision agriculture is to offer subsidies for the definition of management units for posterior interventions. Therefore, the objective of this work was to evaluate soil chemical attributes and sugarcane yield with the use of geostatistics and data mining by decision tree induction. Sugarcane yield was mapped in a 23ha field, applying the cell criterion, by using a yield monitor that allowed the elaboration of a digital map representing the surface of production of the studied area. To determine the soil attributes, soil samples were collected at the beginning of the harvest in 2006/2007 using a regular grid of 50 x 50m, in the depths of 0.0-0.2m and 0.2-0.4m. Soil attributes and sugarcane yield data were analyzed by using geostatistics techniques and were classified into three yield levels for the elaboration of the decision tree. The decision tree was induced in the software SAS Enterprise Miner, using an algorithm based on entropy reduction. Altitude and potassium presented the highest values of correlation with sugarcane yield. 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