High-utility Sequential Rule Mining Utilizing Segmentation Guided by Confidence
Chunkai Zhang, Jiarui Deng, Maohua Lyu, Wensheng Gan, Philip S. Yu
TL;DR
This work tackles the inefficiency of high-utility sequential rule mining (HUSRM) caused by redundant utility computations in left-right expansion strategies. It introduces RSC, a segmentation-guided approach that precomputes rule confidence and generates all rules from a single sequence, enabled by a novel utility-linked table (ULT) and a sequence record table (SRT). A tighter upper bound, reduced remaining utility (RRU), and related pruning strategies further prune the search but preserve completeness for totally ordered rules. Empirical results on real and synthetic datasets show substantial runtime and memory improvements over state-of-the-art methods, validating RSC's effectiveness for large-scale HUSRM tasks.
Abstract
Within the domain of data mining, one critical objective is the discovery of sequential rules with high utility. The goal is to discover sequential rules that exhibit both high utility and strong confidence, which are valuable in real-world applications. However, existing high-utility sequential rule mining algorithms suffer from redundant utility computations, as different rules may consist of the same sequence of items. When these items can form multiple distinct rules, additional utility calculations are required. To address this issue, this study proposes a sequential rule mining algorithm that utilizes segmentation guided by confidence (RSC), which employs confidence-guided segmentation to reduce redundant utility computation. It adopts a method that precomputes the confidence of segmented rules by leveraging the support of candidate subsequences in advance. Once the segmentation point is determined, all rules with different antecedents and consequents are generated simultaneously. RSC uses a utility-linked table to accelerate candidate sequence generation and introduces a stricter utility upper bound, called the reduced remaining utility of a sequence, to address sequences with duplicate items. Finally, the proposed RSC method was evaluated on multiple datasets, and the results demonstrate improvements over state-of-the-art approaches.