EpisodeSupport: A Global Constraint for Mining Frequent Patterns in a Long Sequence of Events

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10848)


The number of applications generating sequential data is exploding. This work studies the discovering of frequent patterns in a large sequence of events, possibly time-stamped. This problem is known as the Frequent Episode Mining (FEM). Similarly to the mining problems recently tackled by Constraint Programming (CP), FEM would also benefit from the modularity offered by CP to accommodate easily additional constraints on the patterns. These advantages do not offer a guarantee of efficiency. Therefore, we introduce two global constraints for solving FEM problems with or without time consideration. The time-stamped version can accommodate gap and span constraints on the matched sequences. Our experiments on real data sets of different levels of complexity show that the introduced constraints is competitive with the state-of-the-art methods in terms of execution time and memory consumption while offering the flexibility of adding constraints on the patterns.


Global Constraints Frequent Episode Mining (FEM) Span Constraint Opposite Vectors Cache Position 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Université catholique de LouvainLouvain-La-NeuveBelgium

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