An evolutionary algorithm for the discovery of rare class association rules in learning management systems

Abstract

Association 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.