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Relational Concept Analysis for Relational Data Exploration

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Advances in Knowledge Discovery and Management

Part of the book series: Studies in Computational Intelligence ((SCI,volume 615))

Abstract

Relational Concept Analysis (RCA) is an extension to the Formal Concept Analysis (FCA) which is an unsupervised classification method producing concept lattices. In addition RCA considers relations between objects from different contexts and builds a set of connected lattices. This feature makes it more intuitive to extract knowledge from relational data and gives richer results. However, data with many relations imply scalability problems and numerous results that are difficult to exploit. We propose in this article a possible adaptation of RCA to explore relations in a guided way in order to increase the performance and the pertinence of the results. We also present an application of exploratory RCA to environmental data for extracting knowledge on water quality of watercourses.

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Notes

  1. 1.

    http://engees-fresqueau.unistra.fr/presentation.php?lang=en.

  2. 2.

    The term taxon covers diverse terms used for the denomination of living beings such as species, genus or families.

  3. 3.

    http://www.sandre.eaufrance.fr.

  4. 4.

    Considering the size of the example, this rule is to be considered for illustrative purpose only.

  5. 5.

    http://dolques.free.fr/rcaexplore/.

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Acknowledgments

We would like to thank C. Grac (ENGEES-LIVE) in particular for her expertise on the provided data and the Fresqueau project ANR11_MONU14 which partially funded this work.

Author information

Authors and Affiliations

  1. ICUBE, Université de Strasbourg/ENGEES, CNRS, Strasbourg, France

    Xavier Dolques & Florence Le Ber

  2. LIRMM, CNRS and Université de Montpellier 2, Montpellier, France

    Marianne Huchard & Clémentine Nebut

Authors
  1. Xavier Dolques
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  2. Florence Le Ber
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  3. Marianne Huchard
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  4. Clémentine Nebut
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Corresponding author

Correspondence to Xavier Dolques .

Editor information

Editors and Affiliations

  1. LINA (CNRS UMR 6241), Polytech'Nantes, Nantes University, Nantes Cedex 3, France

    Fabrice Guillet

  2. LaBRI (CNRS UMR 5800), Bordeaux University, Talence Cedex, France

    Bruno Pinaud

  3. Polytech'Tours, François Rabelais Tours University, Tours, France

    Gilles Venturini

  4. ERIC, Lyon 2 University, Bron, France

    Djamel Abdelkader Zighed

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Dolques, X., Le Ber, F., Huchard, M., Nebut, C. (2016). Relational Concept Analysis for Relational Data Exploration. In: Guillet, F., Pinaud, B., Venturini, G., Zighed, D. (eds) Advances in Knowledge Discovery and Management. Studies in Computational Intelligence, vol 615. Springer, Cham. https://doi.org/10.1007/978-3-319-23751-0_4

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  • DOI: https://doi.org/10.1007/978-3-319-23751-0_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23750-3

  • Online ISBN: 978-3-319-23751-0

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