THUE: Discovering Top-K High Utility Episodes
Shicheng Wan, Jiahui Chen, Wensheng Gan, Guoting Chen, and Vikram, Goyal

TL;DR
This paper introduces THUE, a novel algorithm for efficiently discovering the top-K high utility episodes in complex event sequences, significantly improving speed and reducing memory usage compared to existing methods.
Contribution
The paper presents a new top-K high utility episode mining algorithm with threshold-raising strategies and optimized bounds, enhancing efficiency and practicality.
Findings
THUE achieves 6-8 orders of magnitude faster performance.
The algorithm reduces memory consumption significantly.
Experimental results validate its effectiveness on real and synthetic data.
Abstract
Episode discovery from an event is a popular framework for data mining tasks and has many real-world applications. An episode is a partially ordered set of objects (e.g., item, node), and each object is associated with an event type. This episode can also be considered as a complex event sub-sequence. High-utility episode mining is an interesting utility-driven mining task in the real world. Traditional episode mining algorithms, by setting a threshold, usually return a huge episode that is neither intuitive nor saves time. In general, finding a suitable threshold in a pattern-mining algorithm is a trivial and time-consuming task. In this paper, we propose a novel algorithm, called Top-K High Utility Episode (THUE) mining within the complex event sequence, which redefines the previous mining task by obtaining the K highest episodes. We introduce several threshold-raising strategies and…
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Taxonomy
TopicsData Mining Algorithms and Applications · Algorithms and Data Compression · Imbalanced Data Classification Techniques
