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We consider the task of finding frequent parallel episodes in parallel point processes (or event sequences), allowing for imprecise synchrony of the events constituting occurrences (temporal imprecision) as well as incomplete occurrences (selective participation). The temporal imprecision problem is tackled by frequent pattern mining using a graded notion of synchrony that captures both the number of instances of a pattern as well as the precision of synchrony of its events. To cope with selective participation, a reduction sequence of items (or event types) is formed based on found frequent patterns and guided by pattern overlap. We evaluate the performance of this method on a large number of data sets with injected parallel episodes. We demonstrate that, in contrast to binary synchrony where it pays to consider the pattern instances, graded synchrony performs better with a pattern-based scheme than with an instance-based one, thus simplifying the procedure.

Mining Significant Frequent Patterns in Parallel Episodes with a Graded Notion of Synchrony and Selective Participation