Guided Exploration of Sequential Rules
Wensheng Gan, Gengsen Huang, Junyu Ren, Philip S. Yu

TL;DR
This paper presents a novel, efficient method for user-centric sequential rule discovery in pattern mining, utilizing pruning strategies and similarity metrics to improve runtime, memory usage, and relevance of results.
Contribution
The paper introduces a new approach for targeted sequential rule mining that incorporates pruning and similarity metrics, enhancing efficiency and relevance over existing methods.
Findings
Outperforms state-of-the-art in runtime and memory efficiency
Discovers concise, relevant rules with flexible similarity measures
Effective in personalized sequence data analysis
Abstract
In pattern mining, sequential rules provide a formal framework to capture the temporal relationships and inferential dependencies between items. However, the discovery process is computationally intensive. To obtain mining results efficiently and flexibly, many methods have been proposed that rely on specific evaluation metrics (i.e., ensuring results meet minimum threshold requirements). A key issue with these methods, however, is that they generate many sequential rules that are irrelevant to users. Such rules not only incur additional computational overhead but also complicate downstream analysis. In this paper, we investigate how to efficiently discover user-centric sequential rules. The original database is first processed to determine whether a target query rule is present. To prune unpromising items and avoid unnecessary expansions, we design tight and generalizable upper bounds.…
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Taxonomy
TopicsData Mining Algorithms and Applications · Rough Sets and Fuzzy Logic · AI-based Problem Solving and Planning
