Incremental Relational Association Rule Mining of Educational Data Sets

  • L.M. Crivei Department of Computer Science, Faculty of Mathematics and Computer Science, Babeș-Bolyai University, Cluj-Napoca, Romania


Educational Data Mining is an attractive research field in which the underlying idea is that of bringing the data mining perspective into educational environments. The main focus is to better understand the educational related phenomena by extracting, through data mining techniques, meaningful hidden patterns from educational data sets. Incremental Relational Association Rule Mining (IRARM) has been introduced as an effective online data mining method for dynamically mining interesting relational association rules (RARs) in a dynamic data set which is extended with new data instances. The study conducted in this paper is aimed to emphasize the effectiveness of both RAR and IRARM mining methods in educational data mining settings. Experiments performed on various academic data sets highlight the potential of using relational association rules for uncovering relevant knowledge from educational related data.

How to Cite
CRIVEI, L.M.. Incremental Relational Association Rule Mining of Educational Data Sets. Studia Universitatis Babeș-Bolyai Informatica, [S.l.], v. 63, n. 2, p. 102-117, june 2018. ISSN 2065-9601. Available at: <>. Date accessed: 27 may 2024. doi: