Efficiency of mining algorithms in academic indicators
Data Mining is the process of analyzing data using automated methodologies to find hidden patterns [1]. Data mining processes aim at the use of the dataset generated by a process or business in order to obtain information that supports decision making at executive levels [2] [3] through the automati...
- Autores:
-
amelec, viloria
Hernandez Palma, Hugo Gaspar
Niebles Núñez, William
Gaitán, Mercedes
Pineda Lezama, Bonerge
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/6186
- Acceso en línea:
- https://hdl.handle.net/11323/6186
https://repositorio.cuc.edu.co/
- Palabra clave:
- Data Mining
Mining algorithms
Academic indicators
- Rights
- openAccess
- License
- CC0 1.0 Universal
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Efficiency of mining algorithms in academic indicators |
| title |
Efficiency of mining algorithms in academic indicators |
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Efficiency of mining algorithms in academic indicators Data Mining Mining algorithms Academic indicators |
| title_short |
Efficiency of mining algorithms in academic indicators |
| title_full |
Efficiency of mining algorithms in academic indicators |
| title_fullStr |
Efficiency of mining algorithms in academic indicators |
| title_full_unstemmed |
Efficiency of mining algorithms in academic indicators |
| title_sort |
Efficiency of mining algorithms in academic indicators |
| dc.creator.fl_str_mv |
amelec, viloria Hernandez Palma, Hugo Gaspar Niebles Núñez, William Gaitán, Mercedes Pineda Lezama, Bonerge |
| dc.contributor.author.spa.fl_str_mv |
amelec, viloria Hernandez Palma, Hugo Gaspar Niebles Núñez, William Gaitán, Mercedes Pineda Lezama, Bonerge |
| dc.subject.spa.fl_str_mv |
Data Mining Mining algorithms Academic indicators |
| topic |
Data Mining Mining algorithms Academic indicators |
| description |
Data Mining is the process of analyzing data using automated methodologies to find hidden patterns [1]. Data mining processes aim at the use of the dataset generated by a process or business in order to obtain information that supports decision making at executive levels [2] [3] through the automation of the process of finding predictable information in large databases and answer to questions that traditionally required intense manual analysis [4]. Due to its definition, data mining is applicable to educational processes, and an example of that is the emergence of a research branch named Educational Data Mining, in which patterns and prediction search techniques are used to find information that contributes to improving educational quality [5]. This paper presents a performance study of data mining algorithms: Decision Tree and Logistic Regression, applied to data generated by the academic function at a higher education institution. |
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2020-04-15T17:00:57Z |
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2020 |
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eng |
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eng |
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[1] Han, Jiawei. Introduction to Data Mining. San Francisco: Morgan Kaufmann, 2006. págs. 1-20. [2] Jain, Mugdha, and Chakradhar Verma. "Adapting k-means for Clustering in Big Data." International Journal of Computer Applications 101.1 (2014): 19-24. [3] Huebner, Richard. A survey of educational data-mining research. Norwich: Norwich University, 2013. pág. 13. [4] Maclennan, Jamie. Data Mining with Microsoft SQL Server 2008. Indianapolis, EEUU, Wiley Publishing Inc. 2008. págs. 39-53. [5] Vallejos, Sofía. Minería de Datos. Corrientes, Argentina, Universidad Nacional de Noreste, 2006, págs. 11-16. [6] Viloria, A. "Commercial strategies providers pharmaceutical chains for logistics cost reduction." Indian Journal of Science and Technology 8, no. 1 (2016). [7] Viloria, A., & Gaitan-Angulo, M. (2016). Statistical Adjustment Module Advanced Optimizer Planner and SAP Generated the Case of a Food Production Company. Indian Journal Of Science And Technology, 9(47). doi:10.17485/ijst/2016/v9i47/107371. [8] Karatzoglou A., Smola A., Hornik K. and Zeileis A. kernlab - An S4 Package for Kernel Methods in R. Journal of Statistical Software, 11(9), 1-20, 2004. [9] N. Sapankevych y R. Sankar, “Time Series Prediction Using Support Vector Machines: A Survey”, IEEE Computational Intelligence Magazine, vol. 4, núm. 2, pp. 24–38, may 2009. [10] F. Villada, N. Muñoz, y E. García, Aplicación de las Redes Neuronales al Pronóstico de Precios en Mercado de Valores, Información tecnológica, vol. 23, núm. 4, pp. 11–20. 2012. [11] Venugopal K, K.G. Srinivasa and L. M. Patnaik. Soft Computing for Data Mining Applications. Springer Berlin Heidelberg: Springer-Verlag. ISBN 978-3-642-00192-5, pp 354, 2009. [12] F. Villada, N. Muñoz, y E. García, Aplicación de las Redes Neuronales al Pronóstico de Precios en Mercado de Valores, Información tecnológica, vol. 23, núm. 4, pp. 11–20. 2012. [13] Chapman B, G. Jost and R Van der Pas. Using OpenMP: Portable Shared Memory Parallel Programming Scientific and Engineering Computation. The MIT Press.Massachusetts Institutte of Technology. ISBN 978-0- 262-53302-7. pp 349. 2008. [14] Jain, Mugdha, and Chakradhar Verma. "Adapting k-means for Clustering in Big Data." International Journal of Computer Applications 101.1 (2014): 19-24. [15] Ceruto T, O. Lapeira, A. Rosete and R. ESPÍN.Discovery of fuzzy predicates in database. Advances in Intelligent Systems Research (AISR Journal), vol. 51, No 1, pp. 45-54, ISSN 19516851, Atlantis Press, 2013. [16] Amelec, V., & Alexander, P. (2015). Improvements in the automatic distribution process of finished product for pet food category in multinational company. Advanced Science Letters, 21(5), 1419-1421. [16] Ruß G. Data Mining of Agricultural Yield Data: A Comparison of Regression Models, In: Perner P. (eds) Advances in Data Mining. Applications and Theoretical Aspects, ICDM 2009. Lecture Notes in Computer Science, vol 5633. [17] Taylor S. and Letham B. prophet: Automatic Forecasting Procedure. R package version 0.1. 2017 [18] Wuo W., Xue H. An incorporative statistic and neural approach for crop yield modelling and forecasting, Neural Computing and Applications, 21(1): 109–117, 2012. [19] Ji, B., Sun Y., Yang S. and Wan J. Artificial neural networks for rice yield prediction in mountainous regions, Journal of Agricultural Science, 145: 249-26, 2007. [20] Karatzoglou A., Smola A., Hornik K. and Zeileis A. kernlab - An S4 Package for Kernel Methods in R. Journal of Statistical Software, 11(9), 1-20, 2004 |
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amelec, viloriaHernandez Palma, Hugo GasparNiebles Núñez, WilliamGaitán, MercedesPineda Lezama, Bonerge2020-04-15T17:00:57Z2020-04-15T17:00:57Z20201742-65881742-6596https://hdl.handle.net/11323/6186doi:10.1088/1742-6596/1432/1/012030Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Data Mining is the process of analyzing data using automated methodologies to find hidden patterns [1]. Data mining processes aim at the use of the dataset generated by a process or business in order to obtain information that supports decision making at executive levels [2] [3] through the automation of the process of finding predictable information in large databases and answer to questions that traditionally required intense manual analysis [4]. Due to its definition, data mining is applicable to educational processes, and an example of that is the emergence of a research branch named Educational Data Mining, in which patterns and prediction search techniques are used to find information that contributes to improving educational quality [5]. This paper presents a performance study of data mining algorithms: Decision Tree and Logistic Regression, applied to data generated by the academic function at a higher education institution.amelec, viloria-will be generated-orcid-0000-0003-2673-6350-600Hernandez Palma, Hugo Gaspar-will be generated-orcid-0000-0002-3873-0530-600Niebles Núñez, WilliamGaitán, MercedesPineda Lezama, BonergeengJournal of Physics: Conference SeriesCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Data MiningMining algorithmsAcademic indicatorsEfficiency of mining algorithms in academic indicatorsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion[1] Han, Jiawei. Introduction to Data Mining. San Francisco: Morgan Kaufmann, 2006. págs. 1-20.[2] Jain, Mugdha, and Chakradhar Verma. "Adapting k-means for Clustering in Big Data." International Journal of Computer Applications 101.1 (2014): 19-24.[3] Huebner, Richard. A survey of educational data-mining research. Norwich: Norwich University, 2013. pág. 13.[4] Maclennan, Jamie. Data Mining with Microsoft SQL Server 2008. Indianapolis, EEUU, Wiley Publishing Inc. 2008. págs. 39-53.[5] Vallejos, Sofía. Minería de Datos. Corrientes, Argentina, Universidad Nacional de Noreste, 2006, págs. 11-16.[6] Viloria, A. "Commercial strategies providers pharmaceutical chains for logistics cost reduction." Indian Journal of Science and Technology 8, no. 1 (2016).[7] Viloria, A., & Gaitan-Angulo, M. (2016). Statistical Adjustment Module Advanced Optimizer Planner and SAP Generated the Case of a Food Production Company. Indian Journal Of Science And Technology, 9(47). doi:10.17485/ijst/2016/v9i47/107371.[8] Karatzoglou A., Smola A., Hornik K. and Zeileis A. kernlab - An S4 Package for Kernel Methods in R. Journal of Statistical Software, 11(9), 1-20, 2004.[9] N. Sapankevych y R. Sankar, “Time Series Prediction Using Support Vector Machines: A Survey”, IEEE Computational Intelligence Magazine, vol. 4, núm. 2, pp. 24–38, may 2009.[10] F. Villada, N. Muñoz, y E. García, Aplicación de las Redes Neuronales al Pronóstico de Precios en Mercado de Valores, Información tecnológica, vol. 23, núm. 4, pp. 11–20. 2012.[11] Venugopal K, K.G. Srinivasa and L. M. Patnaik. Soft Computing for Data Mining Applications. Springer Berlin Heidelberg: Springer-Verlag. ISBN 978-3-642-00192-5, pp 354, 2009.[12] F. Villada, N. Muñoz, y E. García, Aplicación de las Redes Neuronales al Pronóstico de Precios en Mercado de Valores, Información tecnológica, vol. 23, núm. 4, pp. 11–20. 2012.[13] Chapman B, G. Jost and R Van der Pas. Using OpenMP: Portable Shared Memory Parallel Programming Scientific and Engineering Computation. The MIT Press.Massachusetts Institutte of Technology. ISBN 978-0- 262-53302-7. pp 349. 2008.[14] Jain, Mugdha, and Chakradhar Verma. "Adapting k-means for Clustering in Big Data." International Journal of Computer Applications 101.1 (2014): 19-24.[15] Ceruto T, O. Lapeira, A. Rosete and R. ESPÍN.Discovery of fuzzy predicates in database. Advances in Intelligent Systems Research (AISR Journal), vol. 51, No 1, pp. 45-54, ISSN 19516851, Atlantis Press, 2013.[16] Amelec, V., & Alexander, P. (2015). Improvements in the automatic distribution process of finished product for pet food category in multinational company. Advanced Science Letters, 21(5), 1419-1421.[16] Ruß G. Data Mining of Agricultural Yield Data: A Comparison of Regression Models, In: Perner P. (eds) Advances in Data Mining. Applications and Theoretical Aspects, ICDM 2009. Lecture Notes in Computer Science, vol 5633.[17] Taylor S. and Letham B. prophet: Automatic Forecasting Procedure. R package version 0.1. 2017[18] Wuo W., Xue H. An incorporative statistic and neural approach for crop yield modelling and forecasting, Neural Computing and Applications, 21(1): 109–117, 2012.[19] Ji, B., Sun Y., Yang S. and Wan J. Artificial neural networks for rice yield prediction in mountainous regions, Journal of Agricultural Science, 145: 249-26, 2007.[20] Karatzoglou A., Smola A., Hornik K. and Zeileis A. kernlab - An S4 Package for Kernel Methods in R. 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