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Mining Contrast Sequential Patterns with ASP

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AIxIA 2023 – Advances in Artificial Intelligence (AIxIA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14318))

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Abstract

In this paper we address an extension of the sequential pattern mining problem which aims at detecting the significant differences between frequent sequences with respect to given classes. The resulting problem is known as contrast sequential pattern mining, since it merges the two notions of sequential pattern and contrast pattern. For this problem we present a declarative approach based on Answer Set Programming (ASP). The efficiency and the scalability of the ASP encoding are evaluated on two publicly available datasets, iPRG and UNIX User, by varying parameters, also in comparison with a hybrid ASP-based approach.

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Notes

  1. 1.

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

  2. 2.

    https://archive.ics.uci.edu/ml/datasets/UNIX+User+Data.

  3. 3.

    https://github.com/mpia3/Contrast-Sequential-Pattern-Mining.git.

  4. 4.

    The meaning of each item can be found at the link where the dataset is published.

  5. 5.

    The reader can find the conversion table at the link where the dataset is published.

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Acknowledgments

This work was partially supported by the project FAIR - Future AI Research (PE00000013), under the NRRP MUR program funded by the NextGenerationEU.

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Authors and Affiliations

  1. DIB and CILA, University of Bari Aldo Moro, Bari, Italy

    Francesca Alessandra Lisi

  2. DMMM, Polytechnic University of Bari, Bari, Italy

    Gioacchino Sterlicchio

Authors
  1. Francesca Alessandra Lisi
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  2. Gioacchino Sterlicchio
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Corresponding author

Correspondence to Francesca Alessandra Lisi .

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Editors and Affiliations

  1. University of Rome Tor Vergata, Rome, Italy

    Roberto Basili

  2. Sapienza University of Rome, Rome, Italy

    Domenico Lembo

  3. Roma Tre University, Rome, Italy

    Carla Limongelli

  4. National Research Council, Rome, Italy

    Andrea Orlandini

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Lisi, F.A., Sterlicchio, G. (2023). Mining Contrast Sequential Patterns with ASP. In: Basili, R., Lembo, D., Limongelli, C., Orlandini, A. (eds) AIxIA 2023 – Advances in Artificial Intelligence. AIxIA 2023. Lecture Notes in Computer Science(), vol 14318. Springer, Cham. https://doi.org/10.1007/978-3-031-47546-7_4

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  • DOI: https://doi.org/10.1007/978-3-031-47546-7_4

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  • Online ISBN: 978-3-031-47546-7

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