Approximate Computation of Exact Association Rules

Saurabh BansalSaurabh Bansal

ISBN: 978-3-030-77866-8
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Saurabh Bansal
Approximate Computation of Exact Association Rules
In Braud, A., Buzmakov, A., Hanika, T., Le Ber, F., eds., Formal Concept Analysis. ICFCA 2021, volume 12733 of Lecture Notes in Artificial Intelligence, 107–122, June 2021. Springer

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KurzfassungAbstract
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.
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Forschungsgruppe:Research Group: Wissensbasierte SystemeKnowledge-Based Systems

The final publication is available at Springer via http://dx.doi.org/https://doi.org/10.1007/978-3-030-77867-5_7.

@InProceedings{10.1007/978-3-030-77867-5_7,
author="Bansal, Saurabh
and Kailasam, Sriram
and Obiedkov, Sergei",
editor="Braud, Agn{\`e}s
and Buzmakov, Aleksey
and Hanika, Tom
and Le Ber, Florence",
title="Approximate Computation of Exact Association Rules",
booktitle="Formal Concept Analysis",
year="2021",
publisher="Springer International Publishing",
address="Cham",
pages="107--122",
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.",
isbn="978-3-030-77867-5"
}