The knowledge discovery through the student's higher education census data

The Higher Education Institutions have become the object of renewed interest [1, 2] as they hold the key to the knowledge-based economy and society [3] and have encompassed what is considered their third mission. They are confronted with a more diverse generation of students [4], identified by a heterogeneous group concerning age, sex, socioeconomic status, race, ethnic, motivation, expectations and personal projects [5]. Concerning that knowledge is the main driver for future social and economic development in the Knowledge Economy and Society, this paper adopts an inductive and exploratory methodological approach to analyze the student's data from Brazilian Higher Education Census. It uses Self-Organizing Maps (SOM) that is a type of neural network which deals which a massive volume of data to explore patterns hidden in the data, focusing on knowledge discovery. The results allow us to visualize five different profiles, based on their OECD main areas of preference, dependence on financial funding, genre and academic status (entrant, enrolled and undergraduate).