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Mining Time-constrained Sequential Patterns with Constraint Programming

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Abstract

Constraint Programming (CP) has proven to be an effective platform for constraint based sequence mining. Previous work has focused on standard frequent sequence mining, as well as frequent sequence mining with a maximum ’gap’ between two matching events in a sequence. The main challenge in the latter is that this constraint can not be imposed independently of the omnipresent frequency constraint. Indeed, the gap constraint changes whether a subsequence is included in a sequence, and hence its frequency. In this work, we go beyond that and investigate the integration of timed events and constraining the minimum/maximum gap as well as minimum/maximum span. The latter constrains the allowed time between the first and last matching event of a pattern. We show how the three are interrelated, and what the required changes to the frequency constraint are. Key in our approach is the concept of an extension window defined by gap/span and we develop techniques to avoid scanning the sequences needlessly, as well as using a backtracking-aware data structure. Experiments demonstrate that the proposed approach outperforms both specialized and CP-based approaches in almost all cases and that the advantage increases as the minimum frequency threshold decreases. This paper is an extension of the original manuscript presented at CPAIOR’17 [5].

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Notes

  1. Except some CP-Solvers such as Gecode, Oz/Mozart and Figaro.

  2. It is neither post-processing nor hard-coded

  3. http://sites.uclouvain.be/cp4dm/spm/ppict/

  4. https://sites.google.com/site/cp4spm/

  5. http://www.cs.rpi.edu/~zaki/www-new/pmwiki.php/Software

  6. http://sites.uclouvain.be/cp4dm/spm/

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Acknowledgements

The research is supported by the FRIA-FNRS (Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture, Belgium) and FWO (Research Foundation – Flanders).

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Authors and Affiliations

  1. Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), Université catholique de Louvain (UCLouvain), Louvain-la-Neuve, Belgium

    John O. R. Aoga & Pierre Schaus

  2. Ecole Doctorale Science de l’Ingénieur (ED-SDI), Université d’Abomey-Calavi (UAC), Calavi, Bénin

    John O. R. Aoga

  3. Vrije Universiteit Brussel (VUB), Brussels, Belgium

    Tias Guns

  4. Katholieke Universiteit Leuven, Leuven, Belgium

    Tias Guns

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  1. John O. R. Aoga
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  2. Tias Guns
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Correspondence to John O. R. Aoga.

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This article belongs to the Topical Collection: Integration of Artificial Intelligence and Operations Research Techniques in Constraint Programming

Guest Editors: Michele Lombardi and Domenico Salvagnin

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Aoga, J.O.R., Guns, T. & Schaus, P. Mining Time-constrained Sequential Patterns with Constraint Programming. Constraints 22, 548–570 (2017). https://doi.org/10.1007/s10601-017-9272-3

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