Data mining algorithms analysis and application
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Abstract
Data Mining is used in different disciplines for searching for hidden patterns and models in databases. This is usually applied in the areas of business and marketing. However, its application and use are finally made available to those who handle this knowledge, so it must be transformed into useful information for the higher levels. Materials and methods. One of the best-known methods for describing attributes in a database is Decision Table, Decision Tree, Linear Regression, and M5. Conclusion. Thirteen attributes were taken from a wine crop, which was discriminated against and then grouped into a set called chemicals. The optimal one within this group turned out to be the total phenols according to the algorithms applied. Therefore, it is the most recommended to use for cultivation
References
Bezerra (2009). f, wainer j, and v. D. Aaalst, "anomaly detection using process mining. Lecture notes in business information processing," vol. 29, p. 12, 2009.
C. M. Tomás, (2011) "desarrollo y análisis de la utilización de algoritmos de minería de datos para la búsqueda de anomalías y patrones secuenciales en minería de
procesos," pontificia universidad catolica de chile escuela de ingenieria p. 167, 2011
Forina, (1991) parvus, "using chemical analysis determine the origin of wines," machine learning repository, 1991.
García martínez, (1997). "sistemas autónomos: aprendizaje automático," nueva librería, buenos aires, argentina, 1997.
García, (2012)"tecnicas de minería de datos basadas en aprendizaje automatico."
José m. Molina and j. "técnicas de minería de datos basadas en aprendizaje automático," 2012.
Jeffrey w. (2010) , "data mining: an overview," congressional research service ˜ the library of congress, vol. 19
L. C. Peñuela, (2013)."algoritmos para mineria de datos con redes neuraonales " universidad politécnica de madrid facultad de informática p. 170, 2013.
Magdalena, (2002) "algoritmos tdidt aplicados a la mineria de datos inteligente," p. 358, 2002
Molina and j. García, (2012). "técnicas de minería de datos basadas en aprendizaje automático," 2012.
Peñuela, (2013) "algoritmos para mineria de datos con redes neuraonales " universidad politécnica de madrid facultad de informática p. 170, 2013.
S. Michalski, a. B. Baskin, and k. A. Spackman, (1982). a logicbased approach to conceptual database analysis, sixth annual symposium on computer applications on
medical care," george washington university, medical center, washington, dc, ee.uu., 1982.
S. Michalski and g. E. Tecuci, (2012). "machine learning: a multistrategy approach," morgan kauffinan, ee.uu, vol. Iv, 1994.microsoft, "data mining algorithms analysis
services - data mining)," microsoft, vol. 4, 2012. "tecnicas de minería de datos basadas en aprendizaje automatico.