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dc.creatoramelec, viloria
dc.creatorHernandez Palma, Hugo Gaspar
dc.creatorNiebles Núñez, William
dc.creatorGaitán, Mercedes
dc.creatorPineda Lezama, Bonerge
dc.date.accessioned2020-04-15T17:00:57Z
dc.date.available2020-04-15T17:00:57Z
dc.date.issued2020
dc.identifier.issn1742-6588
dc.identifier.issn1742-6596
dc.identifier.urihttps://hdl.handle.net/11323/6186
dc.description.abstractData 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.spa
dc.language.isoengspa
dc.publisherJournal of Physics: Conference Seriesspa
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectData Miningspa
dc.subjectMining algorithmsspa
dc.subjectAcademic indicatorsspa
dc.titleEfficiency of mining algorithms in academic indicatorsspa
dc.typeArticlespa
dcterms.references[1] Han, Jiawei. Introduction to Data Mining. San Francisco: Morgan Kaufmann, 2006. págs. 1-20.spa
dcterms.references[2] Jain, Mugdha, and Chakradhar Verma. "Adapting k-means for Clustering in Big Data." International Journal of Computer Applications 101.1 (2014): 19-24.spa
dcterms.references[3] Huebner, Richard. A survey of educational data-mining research. Norwich: Norwich University, 2013. pág. 13.spa
dcterms.references[4] Maclennan, Jamie. Data Mining with Microsoft SQL Server 2008. Indianapolis, EEUU, Wiley Publishing Inc. 2008. págs. 39-53.spa
dcterms.references[5] Vallejos, Sofía. Minería de Datos. Corrientes, Argentina, Universidad Nacional de Noreste, 2006, págs. 11-16.spa
dcterms.references[6] Viloria, A. "Commercial strategies providers pharmaceutical chains for logistics cost reduction." Indian Journal of Science and Technology 8, no. 1 (2016).spa
dcterms.references[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.spa
dcterms.references[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.spa
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dcterms.references[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.spa
dcterms.references[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.spa
dcterms.references[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.spa
dcterms.references[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.spa
dcterms.references[14] Jain, Mugdha, and Chakradhar Verma. "Adapting k-means for Clustering in Big Data." International Journal of Computer Applications 101.1 (2014): 19-24.spa
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dcterms.references[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.spa
dcterms.references[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.spa
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dcterms.references[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, 2004spa
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.doidoi:10.1088/1742-6596/1432/1/012030


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