Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
CPM '01 Proceedings of the 12th Annual Symposium on Combinatorial Pattern Matching
A Practical Algorithm to Find the Best Episode Patterns
DS '01 Proceedings of the 4th International Conference on Discovery Science
Constraint-based mining of episode rules and optimal window sizes
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Reliable detection of episodes in event sequences
Knowledge and Information Systems
Discovering Frequent Closed Partial Orders from Strings
IEEE Transactions on Knowledge and Data Engineering
Discovering Significant Patterns
Machine Learning
Mining Frequent Itemsets in a Stream
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
ACM Transactions on Knowledge Discovery from Data (TKDD)
Significance of Episodes Based on Minimal Windows
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Mining closed episodes with simultaneous events
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining and Knowledge Discovery
Discovering injective episodes with general partial orders
Data Mining and Knowledge Discovery
The long and the short of it: summarising event sequences with serial episodes
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Discovering the most interesting patterns is the key problem in the field of pattern mining. While ranking or selecting patterns is well-studied for itemsets it is surprisingly under-researched for other, more complex, pattern types. In this paper we propose a new quality measure for episodes. An episode is essentially a set of events with possible restrictions on the order of events. We say that an episode is significant if its occurrence is abnormally compact, that is, only few gap events occur between the actual episode events, when compared to the expected length according to the independence model. We can apply this measure as a post-pruning step by first discovering frequent episodes and then rank them according to this measure. In order to compute the score we will need to compute the mean and the variance according to the independence model. As a main technical contribution we introduce a technique that allows us to compute these values. Such a task is surprisingly complex and in order to solve it we develop intricate finite state machines that allow us to compute the needed statistics. We also show that asymptotically our score can be interpreted as a $$P$$P value. In our experiments we demonstrate that despite its intricacy our ranking is fast: we can rank tens of thousands episodes in seconds. Our experiments with text data demonstrate that our measure ranks interpretable episodes high.