AMBIENTUM BIOETHICA BIOLOGIA CHEMIA DIGITALIA DRAMATICA EDUCATIO ARTIS GYMNAST. ENGINEERING EPHEMERIDES EUROPAEA GEOGRAPHIA GEOLOGIA HISTORIA HISTORIA ARTIUM INFORMATICA IURISPRUDENTIA MATHEMATICA MUSICA NEGOTIA OECONOMICA PHILOLOGIA PHILOSOPHIA PHYSICA POLITICA PSYCHOLOGIA-PAEDAGOGIA SOCIOLOGIA THEOLOGIA CATHOLICA THEOLOGIA CATHOLICA LATIN THEOLOGIA GR.-CATH. VARAD THEOLOGIA ORTHODOXA THEOLOGIA REF. TRANSYLVAN
The STUDIA UNIVERSITATIS BABEŞ-BOLYAI issue article summary
The summary of the selected article appears at the bottom of the page. In order to get back to the contents of the issue this article belongs to you have to access the link from the title. In order to see all the articles of the archive which have as author/co-author one of the authors mentioned below, you have to access the link from the author's name.
Authors: LIANA MARIA CRIVEI.
DOI: 10.24193/subbi.2018.2.07
Published Online: 2017-06-19
Published Print: 2017-06-29
pp. 102-117
VIEW PDF: Incremental Relational Association Rule Mining of Educational Data Sets
Abstract. 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.
2010 Mathematics Subject Classification. 68T05, 68P15.
1998 CR Categories and Descriptors. H.2.8[Database management]: Database Applications - Data Mining; I.2.6[Computing Methodologies]: Artificial Intelligence - Learning.
Key words and phrases. Data mining, Educational data mining, Relational association rule, Incremental algorithm.
