  For artificial intelligence, high-utility sequential rule mining (HUSRM) is a
knowledge discovery method that can reveal the associations between events in
the sequences. Recently, abundant methods have been proposed to discover
high-utility sequence rules. However, the existing methods are all related to
point-based sequences. Interval events that persist for some time are common.
Traditional interval-event sequence knowledge discovery tasks mainly focus on
pattern discovery, but patterns cannot reveal the correlation between interval
events well. Moreover, the existing HUSRM algorithms cannot be directly applied
to interval-event sequences since the relation in interval-event sequences is
much more intricate than those in point-based sequences. In this work, we
propose a utility-driven interval rule mining (UIRMiner) algorithm that can
extract all utility-driven interval rules (UIRs) from the interval-event
sequence database to solve the problem. In UIRMiner, we first introduce a
numeric encoding relation representation, which can save much time on relation
computation and storage on relation representation. Furthermore, to shrink the
search space, we also propose a complement pruning strategy, which incorporates
the utility upper bound with the relation. Finally, plentiful experiments
implemented on both real-world and synthetic datasets verify that UIRMiner is
an effective and efficient algorithm.
