  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.
