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Approximate Computation of Exact Association Rules

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Formal Concept Analysis (ICFCA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12733))

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Abstract

We adapt a polynomial-time approximation algorithm for computing the canonical basis of implications to approximately compute frequent implications, also known as exact association rules. To this end, we define a suitable notion of approximation that takes into account the frequency of attribute subsets and show that our algorithm achieves a desired approximation with high probability. We experimentally evaluate the proposed algorithm on several artificial and real-world data sets.

Supported by SPARC, a Government of India Initiative under grant no. SPARC/2018-2019/P682/SL.

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Acknowledgments

We thank Aimene Belfodil for letting us know of the paper [7].

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Authors and Affiliations

  1. IIT Mandi, Mandi, India

    Saurabh Bansal & Sriram Kailasam

  2. HSE University, Moscow, Russia

    Sergei Obiedkov

Authors
  1. Saurabh Bansal
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  2. Sriram Kailasam
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  3. Sergei Obiedkov
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Editor information

Editors and Affiliations

  1. ICube, University of Strasbourg, Illkirch, France

    Agnès Braud

  2. NRU Higher School of Economics, Perm, Russia

    Aleksey Buzmakov

  3. University of Kassel, Kassel, Germany

    Tom Hanika

  4. ICube, University of Strasbourg, Illkirch, France

    Florence Le Ber

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Bansal, S., Kailasam, S., Obiedkov, S. (2021). Approximate Computation of Exact Association Rules. In: Braud, A., Buzmakov, A., Hanika, T., Le Ber, F. (eds) Formal Concept Analysis. ICFCA 2021. Lecture Notes in Computer Science(), vol 12733. Springer, Cham. https://doi.org/10.1007/978-3-030-77867-5_7

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  • DOI: https://doi.org/10.1007/978-3-030-77867-5_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-77866-8

  • Online ISBN: 978-3-030-77867-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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