Mining Association Rules on Time-sensitive Data
Abstract
Real-world data is continuously produced and
collected quickly; hence, mining association rules are used to
find relationships between item sets. One of these combined
rule mining techniques is Rare Association Rule Mining
(RARM), a method that extracts rare, combined rules with low
support 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and combining valid and outdated data leads to low efficiency
in extracting combined rules. This paper explores a mining
method that increases rare, combined rules on time-sensitive
data to extract common and rare patterns in the database.
The primary approach of the research proposes a new tree
structure called ILC-tree, inspired by the LC-tree structure
published in 2022. Specifically, our approach includes a new
tree structure and a fast reconstruction method. The proposed
approach reduces execution time and memory consumption
by approximately 2 times compared to the old tree structure.
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