Knowledge-based model to support decision-making when choosing between two association data mining techniques

Juan Camilo Giraldo Mejía, Diana María Montoya Quintero, Jovani Alberto Jiménez Builes

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 decision-making 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.


Palabras clave


association rules; apriori algorithm; data mining; logistic regression

Texto completo:

PDF


DOI: http://dx.doi.org/10.22507/rli.v14n2a4

Enlaces refback

  • No hay ningún enlace refback.


Copyright (c) 2017 Revista Lasallista de Investigación

Licencia de Creative Commons
Este obra está bajo una licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional.