Mining Frequent Parallel Episodes with Selective Participation
Christian Borgelt, Christian Braune, Kristian Loewe, Rudolf Kruse
Available Online June 2015.
- 10.2991/ifsa-eusflat-15.2015.97How to use a DOI?
- Parallel episode, temporal imprecision, selective participation, frequent pattern mining.
We consider the task of finding frequent parallel episodes in parallel point processes, allowing for imprecise synchrony of the events constituting occurrences (temporal imprecision) as well as incomplete occurrences (selective participation). We tackle this problem with frequent pattern mining based on the CoCoNAD methodology, which is designed to take care of temporal imprecision. To cope with selective participation, we form a reduction sequence of items (event types) based on found frequent patterns and guided by pattern overlap. We evaluate the performance of our method on a large number of data sets with injected parallel episodes.
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Christian Borgelt AU - Christian Braune AU - Kristian Loewe AU - Rudolf Kruse PY - 2015/06 DA - 2015/06 TI - Mining Frequent Parallel Episodes with Selective Participation BT - Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology PB - Atlantis Press SP - 682 EP - 689 SN - 1951-6851 UR - https://doi.org/10.2991/ifsa-eusflat-15.2015.97 DO - 10.2991/ifsa-eusflat-15.2015.97 ID - Borgelt2015/06 ER -