document: A fast algorithm for finding frequent episodes in event streams Frequent episode discovery is a popular framework for mining data available as a long sequence of events . An episode is essentially a short ordered sequence of event types and the frequency of an episode is some suitable measure of how often the episode occurs in the data sequence . Recently , we proposed a new frequency measure for episodes based on the notion of non-overlapped occurrences of episodes in the event sequence , and showed that , such a definition , in addition to yielding computationally efficient algorithms , has some important theoretical properties in connecting frequent episode discovery with HMM learning . This paper presents some new algorithms for frequent episode discovery under this non-overlapped occurrences-based frequency definition . The algorithms presented here are better ( by a factor of N , where N denotes the size of episodes being discovered ) in terms of both time and space complexities when compared to existing methods for frequent episode discovery . We show through some simulation experiments , that our algorithms are very efficient . The new algorithms presented here have arguably the least possible orders of spaceand time complexities for the task of frequent episode discovery .
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