This article contributes to a correlation-based high-utility sequential rule discovery model for data prediction and artificial intelligence analytics. To the best of our knowledge, it is the first article that proposes a realistic correlation-based solution for HUSRM instead of discovering patterns or rules uncorrelated in real-world datasets. The successful application of rule discovery and high-utility pattern mining algorithms can bring great business value in engineering management and profit generation. Since CoUSR can accurately predict the occurrence of sequential patterns with correlation, it can be used in many different applications and domains, such as market basket analysis, risk prediction, and intrusion detection.

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 al...