Mining Frequent Synchronous Patterns with a Graded Notion of Synchrony
Salatiel Ezennaya-Gomez, Christian Borgelt · Advances in intelligent systems research/Advances in Intelligent Systems Research · 2015
We present methods to find (significant) frequent synchronous patterns in event sequences, 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 constituting events.Since transferring earlier work (using a binary notion of synchrony) poses certain problems, we opt for an efficient approximation scheme to compute the pattern support.Furthermore, we transfer methods for filtering for significant and removing induced patterns, which require adaptations.Finally, we demonstrate the effectiveness of our approach with experiments on a large number of data sets with injected synchronous patterns.