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Hybrid ASP-Based Approach to Pattern Mining

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Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 10364))

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Abstract

Detecting small sets of relevant patterns from a given dataset is a central challenge in data mining. The relevance of a pattern is based on user-provided criteria; typically, all patterns that satisfy certain criteria are considered relevant. Rule-based languages like Answer Set Programming (ASP) seem well-suited for specifying such criteria in a form of constraints. Although progress has been made, on the one hand, on solving individual mining problems and, on the other hand, developing generic mining systems, the existing methods either focus on scalability or on generality. In this paper we make steps towards combining local (frequency, size, cost) and global (various condensed representations like maximal, closed, skyline) constraints in a generic and efficient way. We present a hybrid approach for itemset and sequence mining which exploits dedicated highly optimized mining systems to detect frequent patterns and then filters the results using declarative ASP. Experiments on real-world datasets show the effectiveness of the proposed method and computational gains both for itemset and sequence mining.

This work has been partially funded by the ERC AdG SYNTH grant (Synthesising inductive data models).

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Notes

  1. 1.

    In frequent pattern mining, often, a relative threshold, i.e., \(\dfrac{\sigma }{|D|}\) is specified by the user.

  2. 2.

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

  3. 3.

    From https://dtai.cs.kuleuven.be/CP4IM/cpsm/datasets.html.

  4. 4.

    http://potassco.sourceforge.net.

  5. 5.

    https://sites.google.com/site/aspseqmining.

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Authors and Affiliations

  1. Department of Computer Science, KU Leuven, Leuven, Belgium

    Sergey Paramonov

  2. Max Planck Institute of Informatics, Saarbrücken, Germany

    Daria Stepanova & Pauli Miettinen

Authors
  1. Sergey Paramonov
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  2. Daria Stepanova
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  3. Pauli Miettinen
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Corresponding author

Correspondence to Sergey Paramonov .

Editor information

Editors and Affiliations

  1. University of L’Aquila, L’Aquila, Italy

    Stefania Costantini

  2. Free University of Bozen-Bolzano, Bolzano, Italy

    Enrico Franconi

  3. Dalhousie University, Halifax, Nova Scotia, Canada

    William Van Woensel

  4. Birkbeck, University of London, London, United Kingdom

    Roman Kontchakov

  5. Imperial College London, London, United Kingdom

    Fariba Sadri

  6. SINTEF/University of Oslo, Oslo, Norway

    Dumitru Roman

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Paramonov, S., Stepanova, D., Miettinen, P. (2017). Hybrid ASP-Based Approach to Pattern Mining. In: Costantini, S., Franconi, E., Van Woensel, W., Kontchakov, R., Sadri, F., Roman, D. (eds) Rules and Reasoning. RuleML+RR 2017. Lecture Notes in Computer Science(), vol 10364. Springer, Cham. https://doi.org/10.1007/978-3-319-61252-2_14

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  • DOI: https://doi.org/10.1007/978-3-319-61252-2_14

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