Abstract:
Mining high utility episodes in complex event sequences is an emerging
topic in data mining. In utility mining, users set a minimum threshold and
the episodes having higher utility than the threshold are reported. The
utility framework introduced in episode mining provides more informative
and usable knowledge as compared to frequent episode mining. However,
it is difficult for the user to set an appropriate minimum utility thresh-
old. As the user cannot predict the count of mined episodes by the utility
threshold, the number of reported episodes can vary hugely in accordance
to the set threshold. To address this issue, in this thesis we propose an
algorithm for mining Top-K high utility episode in complex event sequence.
It discovers episodes with highest utility to ones with kth highest utility
where the user can set the desired count of episodes, k. We also propose
two different strategies to reduce the search space by raising the minimum
threshold e ectively. We conduct experiments on real dataset and show the
effectiveness of our approach.
