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Mining Frequent Parallel Episodes with Selective Participation

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.