Totally-ordered Sequential Rules for Utility Maximization

Abstract

High-utility sequential pattern mining (HUSPM) is a significant and valuable activity in knowledge discovery and data analytics with many real-world applications. In some cases, HUSPM can not provide an excellent measure to predict what will happen. High-utility sequential rule mining (HUSRM) discovers high utility and high confidence sequential rules, so it can solve the issue in HUSPM. However, all existing HUSRM algorithms aim to find high-utility partially-ordered sequential rules (HUSRs), which are not consistent with reality and may generate fake HUSRs. Therefore, in this paper, we formulate the problem of high-utility totally-ordered sequential rule mining and propose a novel algorithm, called TotalSR, which aims to identify all high-utility totally-ordered sequential rules (HTSRs). TotalSR introduces a left-first expansion strategy that can utilize the anti-monotonic property to use a confidence pruning strategy. TotalSR also designs a new utility upper bound: RSPEU , which is tighter than the existing upper bounds. TotalSR can drastically reduce the search space with the help of utility upper bounds pruning strategies, avoiding much more meaningless computation. To effectively compute the information, TotalSR proposes an auxiliary antecedent record table that can efficiently calculate the antecedent’s support and a utility prefix sum list that can compute the upper bound in O (1) time for a sequence. Finally, there are numerous experimental results on both real and synthetic datasets demonstrating that TotalSR is more efficient than the existing algorithms.