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

TLDR

Least association rules, which capture infrequent events and exceptional cases, are valuable for detecting rare patterns but are difficult to measure, especially in educational settings where they can inform standards and management improvements. The study introduces Critical Relative Support (CRS) as a new metric to mine critical least association rules in educational data. CRS measures the relative support of least frequent itemsets to efficiently discover critical least association rules. Experiments on a student examination dataset demonstrate that CRS reveals significant rules while eliminating up to 98% of irrelevant association rules.

Abstract

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.