THUE: Discovering Top-K High Utility Episodes

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 optimize the episode-weighted utilization upper bounds to speed up the mining process and effectively reduce the memory cost. Finally, the experimental results on both real-life and synthetic datasets reveal that the THUE algorithm can offer six to eight orders of magnitude running time performance improvement over the state-of-the-art algorithm and has low memory consumption.