Abstract

MENES CAMEJO, Iván; ARCOS MEDINA, Gloria  and  GALLEGOS CARRILLO, Katherine. Performance of data mining algorithms in academic indicators: Decision Tree and Logistic Regression . Rev cuba cienc informat [online]. 2015, vol.9, n.4, pp.104-117. ISSN 2227-1899.

Data mining is aimed at prospective reporting, for which is necessary to choose the most appropriate algorithm, i.e. the one that provides the best results, depending on data types and project objectives. In this paper a study of performance of two data mining algorithms is presented, namely Decision Tree and Logistic Regression, which have been applied to continuous and discrete data generated by the academic function of an institution of higher education. We sought to determine the algorithm with the best performance by means of the scientific method and descriptive and inferential statistical techniques. The results show that the decision tree algorithm is the best algorithm in terms of indicators of response time, CPU usage, RAM usage and accuracy.

Keywords : performance analysis; academy indicators; decision tree; logistic regression; data mining.

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