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/event sets can be filtered for closed and maximal sets.