Relational Concept Analysis for Relational Data Exploration

  • Xavier DolquesEmail author
  • Florence Le Ber
  • Marianne Huchard
  • Clémentine Nebut
Part of the Studies in Computational Intelligence book series (SCI, volume 615)


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.



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.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Xavier Dolques
    • 1
    Email author
  • Florence Le Ber
    • 1
  • Marianne Huchard
    • 2
  • Clémentine Nebut
    • 2
  1. 1.ICUBE, Université de Strasbourg/ENGEES, CNRSStrasbourgFrance
  2. 2.LIRMM, CNRS and Université de Montpellier 2MontpellierFrance

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