Mining the applications to study at Lithuania's institutions of higher education using association rules

Abstract

The secondary outcome of implementing the computerized application process for -studies at Lithuania's institutions of higher education is the availability of the collected data. An important problem is how to use this data effectively, e.g., how to extract some useful knowledge from it. Discovered patterns and relationships can facilitate a better understanding of the applicants' preferences, popularity of various study programs, and, more generally, the overall application process. This paper uses the association rule mining technique and focuses on the relationships between the gender of the applicants and their preferred study programs. The data mining procedure, which can also be considered as a framework for similar data mining tasks, is presented through several illustrative examples.