An efficient tree-based rule mining algorithm for sequential rules with explicit timestamps in temporal educational databases

Abstract

In educational data mining, frequent patterns and association rules are popular to help us get insights into the characteristics of the students and their study. Nonetheless, frequent patterns and rules discovered in the existing works are simple with no temporal information along the student’s study paths. Indeed, many sequential pattern and rule mining techniques just considered a sequence of ordered events with no explicit time. In order to achieve sequential rules with explicit timestamps in temporal educational databases that contain timestamp-extended sequences, our work defines a tree-based rule mining algorithm from the frequent sequences generated and organized in a prefix tree enhanced with explicit timestamps. Experimental results on real educational datasets have shown that the proposed algorithm can provide more informative sequential rules with explicit timestamps. Besides, it is more efficient than the brute-force list-based algorithm by optimizing the manipulations on the prefix tree for sequential rules with explicit timestamps.