Efficiency of mining algorithms in academic indicators

Data 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