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- W3177232015 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." @default.
- W3177232015 created "2021-07-05" @default.
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- W3177232015 date "2021-06-28" @default.
- W3177232015 modified "2023-10-18" @default.
- W3177232015 title "THUE: Discovering Top-K High Utility Episodes" @default.
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- W3177232015 doi "https://doi.org/10.48550/arxiv.2106.14830" @default.
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