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Clustering and Identification of Core Implications

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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

FCA exhaustively uses the notion of cluster by grouping attributes and objects and providing a solid algebraic structure to them through the concept lattice. Our proposal explores how we can cluster implications. This work opens a research line to study the knowledge inside the clusters computed from the Duquenne-Guigues basis. Some alternative measures to induce the clusters are analysed, taking into account the information that directly appears in the appearance and the semantics of the implications. This work also allows us to show the fcaR package, which has the main methods of FCA and the Simplification Logic. The paper ends with a motivation of the potential applications of performing clustering on the implications.

Supported by Grants TIN2017-89023-P, UMA2018-FEDERJA-001 and PGC2018-095869-B-I00 of the Junta de Andalucia, and European Social Fund.

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Notes

  1. 1.

    As far as we know, no package using the R language has been developed and published in CRAN repository for FCA, even when the R language together with Python are considered the main languages in data science, machine learning, big data, etc. To this date, fcaR has more than 8,000 downloads.

  2. 2.

    In this work, we do not use all the methods in the fcaR package to manage the formal context, the concept lattice, the concepts, the implications, etc. See https://neuroimaginador.github.io/fcaR/ for more details.

  3. 3.

    See https://neuroimaginador.github.io/fcaR/articles/implications.html.

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Authors and Affiliations

  1. Universidad de Málaga, Málaga, Spain

    Domingo López-Rodríguez, Pablo Cordero, Manuel Enciso & Ángel Mora

Authors
  1. Domingo López-Rodríguez
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  2. Pablo Cordero
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  3. Manuel Enciso
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  4. Ángel Mora
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Corresponding author

Correspondence to Domingo López-Rodríguez .

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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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López-Rodríguez, D., Cordero, P., Enciso, M., Mora, Á. (2021). Clustering and Identification of Core Implications. 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_9

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  • DOI: https://doi.org/10.1007/978-3-030-77867-5_9

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