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CoverSize: A Global Constraint for Frequency-Based Itemset Mining

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Principles and Practice of Constraint Programming (CP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 10416))

Abstract

Constraint Programming is becoming competitive for solving certain data-mining problems largely due to the development of global constraints. We introduce the CoverSize constraint for itemset mining problems, a global constraint for counting and constraining the number of transactions covered by the itemset decision variables. We show the relation of this constraint to the well-known table constraint, and our filtering algorithm internally uses the reversible sparse bitset data structure recently proposed for filtering table. Furthermore, we expose the size of the cover as a variable, which opens up new modelling perspectives compared to an existing global constraint for (closed) frequent itemset mining. For example, one can constrain minimum frequency or compare the frequency of an itemset in different datasets as is done in discriminative itemset mining. We demonstrate experimentally on the frequent, closed and discriminative itemset mining problems that the CoverSize constraint with reversible sparse bitsets allows to outperform other CP approaches.

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Notes

  1. 1.

    This is similar to the update of currTable in [14] for filtering table constraints.

  2. 2.

    As for many NP-hard global constraints like bin-packing, cumulative, circuit, etc.

  3. 3.

    A related generic technique in CP is shaving [26].

  4. 4.

    http://fimi.ua.ac.be/data/.

  5. 5.

    https://dtai.cs.kuleuven.be/CP4IM/datasets/.

  6. 6.

    http://research.nii.ac.jp/~uno/codes.htm (v3 is fastest of all versions in our experiments).

  7. 7.

    https://sites.uclouvain.be/cp4dm/fim/.

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Acknowledgments

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). We also thank Willard Zhan for his help with the reduction proof.

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Authors and Affiliations

  1. UCLouvain, ICTEAM, Louvain-la-Neuve, Belgium

    Pierre Schaus & John O. R. Aoga

  2. UAC, ED-SDI, Abomey-Calavi, Benin

    John O. R. Aoga

  3. VUB Brussels, Brussels, Belgium

    Tias Guns

  4. KU Leuven, Leuven, Belgium

    Tias Guns

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  1. Pierre Schaus
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  2. John O. R. Aoga
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  3. Tias Guns
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Correspondence to Pierre Schaus .

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  1. University of Toronto, Toronto, Ontario, Canada

    J. Christopher Beck

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Schaus, P., Aoga, J.O.R., Guns, T. (2017). CoverSize: A Global Constraint for Frequency-Based Itemset Mining. In: Beck, J. (eds) Principles and Practice of Constraint Programming. CP 2017. Lecture Notes in Computer Science(), vol 10416. Springer, Cham. https://doi.org/10.1007/978-3-319-66158-2_34

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  • DOI: https://doi.org/10.1007/978-3-319-66158-2_34

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