Paper Info
Title
An evolutionary algorithm for the discovery of rare class association rules in learning management systems
Abstract
ociation rule mining, an important data mining technique, has been widely focused on the extraction of frequent patterns. Nevertheless, in some application domains it is interesting to discover patterns that do not frequently occur, even when they are strongly related. More specifically, this type of relation can be very appropriate in e-learning domains due to its intrinsic imbalanced nature. In these domains, the aim is to discover a small but interesting and useful set of rules that could barely be extracted by traditional algorithms founded in exhaustive search-based techniques. In this paper, we propose an evolutionary algorithm for mining rare class association rules when gathering student usage data from a Moodle system. We analyse how the use of different parameters of the algorithm determine the rule characteristics, and provides some illustrative examples of them to show their interpretability and usefulness in e-learning environments. We also compare our approach to other existing algorithms for mining both rare and frequent association rules. Finally, an analysis of the rules mined is presented, which allows information about students' unusual behaviour regarding the achievement of bad or good marks to be discovered.
Year
DOI
Venue
2015
10.1007/s10489-014-0603-4
Applied Intelligence
Keywords
Field
DocType
Rare association rules,Grammar guided genetic programming,Evolutionary computation,Educational data mining
Interpretability,Data mining,Evolutionary algorithm,Brute-force search,Computer science,Evolutionary computation,Association rule learning,Artificial intelligence,Usage data,Educational data mining,K-optimal pattern discovery,Machine learning
Journal
Volume
Issue
ISSN
42
3
0924-669X
Citations 
PageRank 
References 
17
0.78
28
Authors
4
Name
Order
Citations
PageRank
J. M. Luna1171.12
Cristóbal Romero22226148.97
J. R. Romero3462.65
S. Ventura42318158.44