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Título : Knowledge-based model to support decision-making when choosing between two association data mining techniques
Otros títulos : Modelo basado en conocimiento para apoyar la toma de decisiones entre dos técnicas de asociación de la minería de datos
Modelo baseado no conhecimento para apoiar a tomada de decisões entre duas técnicas de associação de mineração de dados
Autor : Giraldo Mejía, Juan Camilo
Montoya Quintero, Diana María
Jiménez Builes, Jovani Alberto
Palabras clave : Corporación Universitaria Lasallista
Minería de datos
Reglas de asociación
Regresión logística
Algoritmo Apriori
Toma de decisiones
Fecha de publicación : 2017
Editorial : Corporación Universitaria Lasallista
Citación : Revista Lasallista de Investigación Vol. 14 N. 2
Resumen : Introduction. This paper presents the functionality and characterization of two Data Mining (DM) techniques, logistic regression and association rules (Apriori Algorithm). This is done through a conceptual model that enables to choose the appropriate data mining project technique for obtaining knowledge from criteria that describe the specific project to be developed. Objective. Support decision making when choosing the most appropriate technique for the development of a data mining project. Materials and methods. Association and logistic regression techniques are characterized in this study, showing the functionality of their algorithms. Results. The proposed model is the input for the implementation of a knowledge-based system that emulates a human expert’s knowledge at the time of deciding which data mining technique to choose against a specific problem that relates to a data mining project. It facilitates verification of the business processes of each one of the techniques, and measures the correspondence between a project’s objectives versus the components provided by both the logistic regression and the association rules techniques. Conclusion. Current and historical information is available for decisionmaking through the generated data mining models. Data for the models are taken from Data Warehouses, which are informational environments that provide an integrated and total view of the organization.
URI : http://hdl.handle.net/10567/1902
ISSN : 1794-4449
Aparece en las colecciones: Revista LASALLISTA de Investigación

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