Approximate Computation of Exact Association Rules

Saurabh BansalSaurabh Bansal, Sriram KailasamSriram Kailasam, Sergei ObiedkovSergei Obiedkov

Saurabh Bansal, Sriram Kailasam, Sergei Obiedkov
Approximate Computation of Exact Association Rules
In Agnès Braud, Aleksey Buzmakov, Tom Hanika, Florence Le Ber, eds., Formal Concept Analysis. ICFCA 2021, volume 12733 of Lecture Notes in Computer Science, 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.
Forschungsgruppe:Research Group: Wissensbasierte SystemeKnowledge-Based Systems

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

@inproceedings{BKO2021,
  author    = {Saurabh Bansal and Sriram Kailasam and Sergei Obiedkov},
  title     = {Approximate Computation of Exact Association Rules},
  editor    = {Agn{\`{e}}s Braud and Aleksey Buzmakov and Tom Hanika and
               Florence Le Ber},
  booktitle = {Formal Concept Analysis. {ICFCA} 2021},
  series    = {Lecture Notes in Computer Science},
  volume    = {12733},
  publisher = {Springer},
  year      = {2021},
  month     = {June},
  pages     = {107-122},
  doi       = {10.1007/978-3-030-77867-5_7}
}