Targeted Mining Precise-positioning Episode Rules
Jian Zhu, Xiaoye Chen, Wensheng Gan, Zefeng Chen, Philip S. Yu
TL;DR
This work defines targeted mining of precise-positioning episode rules and introduces TaMIPER, a two-phase, tree-based algorithm designed to discover complete, accurate TaPERs that satisfy user-defined targets, supports, and confidences. It leverages four pruning strategies (PBPS, DBPS, NBPS, LBPS) to drastically reduce candidate exploration and improve runtime and memory efficiency. Comprehensive experiments on six real datasets demonstrate TaMIPER's superior performance and scalability compared to baselines, with case studies highlighting actionable, domain-relevant rules (e.g., bike-sharing station pairs). The approach enables precise, user-driven rule discovery in complex event sequences, with potential applications in weather, network security, and e-commerce, and points to future work on more sophisticated targeted mining structures and constraints.
Abstract
The era characterized by an exponential increase in data has led to the widespread adoption of data intelligence as a crucial task. Within the field of data mining, frequent episode mining has emerged as an effective tool for extracting valuable and essential information from event sequences. Various algorithms have been developed to discover frequent episodes and subsequently derive episode rules using the frequency function and anti-monotonicity principles. However, currently, there is a lack of algorithms specifically designed for mining episode rules that encompass user-specified query episodes. To address this challenge and enable the mining of target episode rules, we introduce the definition of targeted precise-positioning episode rules and formulate the problem of targeted mining precise-positioning episode rules. Most importantly, we develop an algorithm called Targeted Mining Precision Episode Rules (TaMIPER) to address the problem and optimize it using four proposed strategies, leading to significant reductions in both time and space resource requirements. As a result, TaMIPER offers high accuracy and efficiency in mining episode rules of user interest and holds promising potential for prediction tasks in various domains, such as weather observation, network intrusion, and e-commerce. Experimental results on six real datasets demonstrate the exceptional performance of TaMIPER.