Mining significant association rules from educational data using critical relative support approach

摘要

Least association rules are the association rules that consist of the least item. These rules are very important and critical since they can be used to detect the infrequent events and exceptional cases. However, the formulation of measurement to efficiently discover least association rules is quite intricate and not really straight forward. In educational domain, this information is very useful since it can be used as a base for investigating and enhancing the current educational standards and managements. Therefore, this paper proposes a new measurement called Critical Relative Support (CRS) to mine critical least association rules from educational context. Experiment with students’ examination result dataset shows that this approach can be used to reveal the significant rules and also can reduce up to 98% of uninterested association rules.