Abstract
This article presents the use of Answer Set Programming (ASP) to mine sequential patterns. ASP is a high-level declarative logic programming paradigm for high level encoding combinatorial and optimization problem solving as well as knowledge representation and reasoning. Thus, ASP is a good candidate for implementing pattern mining with background knowledge, which has been a data mining issue for a long time. We propose encodings of the classical sequential pattern mining tasks within two representations of embeddings (fill-gaps versus skip-gaps) and for various kinds of patterns: frequent, constrained and condensed. We compare the computational performance of these encodings with each other to get a good insight into the efficiency of ASP encodings. The results show that the fill-gaps strategy is better on real problems due to lower memory consumption. Finally, compared to a constraint programming approach (CPSM), another declarative programming paradigm, our proposal showed comparable performance.
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- 1.
It is important to notice that the scope of a variable is the rule and each occurrence of a variable in a rule represents the same value.
- 2.
clingo is fully compliant with the recent ASP standard: https://www.mat.unical.it/aspcomp2013/ASPStandardization.
- 3.
A similar encoding can be done for the fill-gaps strategy applying the same changes as above.
- 4.
- 5.
- 6.
The generator and databases used in our experiments are available at https://sites.google.com/site/aspseqmining.
- 7.
The use of subset-minimal heuristic keeps solving the maximal patterns problem complete.
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Acknowledgements
We would like to thanks Roland Kaminski and Max Ostrowski for their significant inputs and comments about ASP encodings; and Benjamin Negrevergne and Tias Guns for their suggestions about the experimental part. We also thank the anonymous reviewers for their valuable comments and constructive suggestions.
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Appendix
Appendix
Listing 3.10 illustrates how the encoding of the skip-gaps strategy can be transformed to mine sequential patterns that are sequences of itemsets.
The first difference with the encoding of Listing 3.2 concerns the generation of patterns. The upper bound constraint of the choice rule in Line 9 has been removed, enabling the possible generation of every non-empty subset of \(\mathscr {I}\).
The second difference is that the new ASP rules verify the inclusion of all items in itemsets. Line 14, seq(T,P,I):pat(1,I) indicates that for each atom pat(1,I) there should exist an atom seq(T,P,I) to satisfy the rule body. A similar expression is used Line 15.
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Guyet, T., Moinard, Y., Quiniou, R., Schaub, T. (2018). Efficiency Analysis of ASP Encodings for Sequential Pattern Mining Tasks. In: Pinaud, B., Guillet, F., Cremilleux, B., de Runz, C. (eds) Advances in Knowledge Discovery and Management. Studies in Computational Intelligence, vol 732. Springer, Cham. https://doi.org/10.1007/978-3-319-65406-5_3
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