Abstract
Knowledge in the form of generalized sequential patterns finds many applications. In this paper, we focus on optimizing GSP, which is a well-known algorithm for discovering such patterns. Our optimization consists in more selective identification of nodes to be visited while traversing a hash tree with candidates for generalized sequential patterns. It is based on the fact that elements of candidate sequences are stored as ordered sets of items. In order to reduce the number of visited nodes in the hash tree, we also propose to use not only parameters windowSize and maxGap as in original GSP, but also parameter minGap. As a result of our optimization, the number of candidates that require final time-consuming verification may be considerably decreased. In the experiments we have carried out, our optimized variant of GSP was several times faster than standard GSP.
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Notes
- 1.
In our implementation of the hash tree, this restriction is kept for leaves at levels not exceeding |Flattened(S)|, but leaves at level |Flattened(S)| + 1 are allowed to store more than m candidate sequences.
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Kryszkiewicz, M., Skonieczny, Ł. (2019). Fast Discovery of Generalized Sequential Patterns. In: Bembenik, R., Skonieczny, Ł., Protaziuk, G., Kryszkiewicz, M., Rybinski, H. (eds) Intelligent Methods and Big Data in Industrial Applications. Studies in Big Data, vol 40. Springer, Cham. https://doi.org/10.1007/978-3-319-77604-0_12
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