THUE: Discovering Top-K High Utility Episodes

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. Highutility 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 reallife 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.