The goal of high-utility sequential pattern mining (HUSPM) is to efficiently
discover profitable or useful sequential patterns in a large number of
sequences. However, simply being aware of utility-eligible patterns is
insufficient for making predictions. To compensate for this deficiency,
high-utility sequential rule mining (HUSRM) is designed to explore the
confidence or probability of predicting the occurrence of consequence
sequential patterns based on the appearance of premise sequential patterns. It
has numerous applications, such as product recommendation and weather
prediction. However, the existing algorithm, known as HUSRM, is limited to
extracting all eligible rules while neglecting the correlation between the
generated sequential rules. To address this issue, we propose a novel algorithm
called correlated high-utility sequential rule miner (CoUSR) to integrate the
concept of correlation into HUSRM. The proposed algorithm requires not only
that each rule be correlated but also that the patterns in the antecedent and
consequent of the high-utility sequential rule be correlated. The algorithm
adopts a utility-list structure to avoid multiple database scans. Additionally,
several pruning strategies are used to improve the algorithm's efficiency and
performance. Based on several real-world datasets, subsequent experiments
demonstrated that CoUSR is effective and efficient in terms of operation time
and memory consumption.Comment: Preprint. 7 figures, 6 table