Finding Frequent Patterns in Parallel Point Processes

We consider the task of finding frequent patterns in parallel point processes--also known as finding frequent parallel episodes in event sequences. This task can be seen as a generalization of frequent item set mining: the co-occurrence of items or events in transactions is replaced by their imprecise co-occurrence on a continuous time scale, meaning that they occur in a limited time span from each other. We define the support of an item set in this setting based on a maximum independent set approach allowing for efficient computation. Furthermore, we show how the enumeration and test of candidate sets can be made efficient by properly reducing the event sequences and exploiting perfect extension pruning. Finally, we study how the resulting frequent item sets/patterns can be filtered for closed and maximal sets.