Mining Association Rules on Time-sensitive Data

Real-world data is continuously produced andcollected quickly; hence, mining association rules are used tofind relationships between item sets. One of these combinedrule mining techniques is Rare Association Rule Mining(RARM), a method that extracts rare, combined rules with lowsupport but high confidence from the database. Additionally,a large amount of time-sensitive data is created in many fields,and combining valid and outdated data leads to low efficiencyin extracting combined rules. This paper explores a miningmethod that increases rare, combined rules on time-sensitivedata to extract common and rare patterns in the database.The primary approach of the research proposes a new treestructure called ILC-tree, inspired by the LC-tree structurepublished in 2022. Specifically, our approach includes a newtree structure and a fast reconstruction method. The proposedapproach reduces execution time and memory consumptionby approximately 2 times compared to the old tree structure.