Relational Concept Analysis for Relational Data Exploration

  • Xavier Dolques
  • Florence Le Ber
  • Marianne Huchard
  • Clémentine Nebut
Chapter
Part of the Studies in Computational Intelligence book series (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.

Notes

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.

References

  1. Azmeh, Z., M. Huchard, A. Napoli, M.R. Hacene, and P. Valtchev. 2011. Querying relational concept lattices. In Proceedings of the 8th International Conference on Concept Lattices and their Applications (CLA’11), 377–392.Google Scholar
  2. Barbut, M., and B. Monjardet. 1970. Ordre et Classification: Algèbre et Combinatoire, vol. 2. Hachette.Google Scholar
  3. Bedel, O., S. Ferré, and O. Ridoux. 2008. Handling spatial relations in logical concept analysis to explore geographical data. In Formal Concept Analysis, vol. 4933, ed. R. Medina, and S. Obiedkov, 241–257, LNCS. Berlin: Springer.Google Scholar
  4. Berry, A., A. Gutierrez, M. Huchard, A. Napoli, and Sigayret, A. 2014. Hermes: a simple and efficient algorithm for building the aoc-poset of a binary relation. Annals of Mathematics and Artificial Intelligence.Google Scholar
  5. Bertaux, A., F. Le Ber, A. Braud, and Trémolières, M. 2009. Identifying ecological traits: a concrete fca-based approach. In 7th International Conference on Formal Concept Analysis, ICFCA 2009, Darmstadt, vol. 5548, eds. S. Ferré, and S. Rudolph, 224–236, LNAI. Springer.Google Scholar
  6. Braud, A., C. Nica, C. Grac, and F. Le Ber. 2011. A lattice-based query system for assessing the quality of hydro-ecosystems. In Proceedings of the 8th International Conference on Concept Lattices and Their Applications (CLA 2001), Nancy, eds. A. Napoli, and V. Vychodil, 265–277. INRIA Nancy-Grand-Est and LORIA.Google Scholar
  7. Carpineto, C., and G. Romano. 1995. Ulysses: a lattice-based multiple interaction strategy retrieval interface. In EWHCI, vol. 1015, Lecture Notes in Computer Science, eds. B. Blumenthal, J. Gornostaev, and C. Unger, 91–104. Springer.Google Scholar
  8. Carpineto, C., and G. Romano. 2004. Concept Data Analysis: Theory and Applications. Wiley.Google Scholar
  9. Collier, K.J., R.J. Ilcock, and A.S. Meredith. 1998. Influence of substrate type and physico-chemical conditions on macroinvertebrate faunas and biotic indices of some lowland Waikato, New Zealand, streams. New Zealand Journal of Marine and Freshwater Research 32(1): 1–19.CrossRefGoogle Scholar
  10. Dolques, X., M. Huchard, and C. Nebut. 2009. From transformation traces to transformation rules: assisting model driven engineering approach with formal concept analysis. In Supplementary Proceedings of ICCS’09, 15–29.Google Scholar
  11. Dolques, X., M. Huchard, C. Nebut, and P. Reitz. 2010. Fixing generalization defects in UML use case diagrams. In CLA’10: 7th International Conference on Concept Lattices and Their Applications, 247–258.Google Scholar
  12. Ducrou, J., B. Wormuth, and P.W. Eklund. 2005. Dynamic schema navigation using formal concept analysis. In DaWaK, vol. 3589, Lecture Notes in Computer Science, eds. A.M. Tjoa, and J. Trujillo, 398–407. Springer.Google Scholar
  13. Fabrègue, M., A. Braud, S. Bringay, F. Le Ber, and M. Teisseire. 2013. OrderSpan: mining closed partially ordered patterns. In The Twelfth International Symposium on Intelligent Data Analysis (IDA 2013), vol. 8207, 186–197, LNCS. London: Springer.Google Scholar
  14. Ferré, S. 2009. Camelis: a logical information system to organise and browse a collection of documents. International Journal of General Systems 38(4): 379–403.MATHCrossRefGoogle Scholar
  15. Ferré, S. 2010. Conceptual navigation in RDF graphs with SPARQL-Like Queries. In ICFCA, vol. 5986, eds. L. Kwuida, and B. Sertkaya,193–208, LNCS. Springer.Google Scholar
  16. Ferré, S., and A. Hermann. 2011. Semantic search: reconciling expressive querying and exploratory search. In International Semantic Web Conference, vol. 7031, eds. L. Aroyo, and C. Welty, 177–192, LNCS Springer.Google Scholar
  17. Ganter, B., and S.O. Kuznetsov. 2001. Pattern structures and their projections. In Proceedings of the 9th International Conference on Conceptual Structures (ICCS 2001), 129–142.Google Scholar
  18. Ganter, B., and R. Wille. 1999. Formal Concept Analysis. Mathematical Foundations: Springer.Google Scholar
  19. Goethals, P.L., A.P. Dedecker, W. Gabriels, S. Lek, and N. Pauw. 2007. Applications of artificial neural networks predicting macroinvertebrates in freshwaters. Aquatic Ecology 41(3): 491–508.CrossRefGoogle Scholar
  20. Hacene, M.R., M. Huchard, A. Napoli, and P. Valtchev. 2013. Relational concept analysis: mining concept lattices from multi-relational data. Annals of Mathematics and Artificial Intelligence 67(1): 81–108.MATHMathSciNetCrossRefGoogle Scholar
  21. Kocev, D., A. Naumoski, K. Mitreski, S. Krstić, and S. Džeroski. 2010. Learning habitat models for the diatom community in lake prespa. Ecological Modelling 221(2): 330–337.CrossRefGoogle Scholar
  22. Kötters, J. 2011. Object configuration browsing in relational databases. In ICFCA, vol. 6628, Lecture Notes in Computer Science, eds. P. Valtchev, and R. Jäschke, 151–166. Springer.Google Scholar
  23. Kuznetsov, S.O., and S.A. Obiedkov. 2002. Comparing performance of algorithms for generating concept lattices. Journal of Experimental and Theoretical Artificial Intelligence 14(2–3): 189–216.MATHCrossRefGoogle Scholar
  24. Lachiche, N. 2010. Propositionalization. In Encyclopedia of Machine Learning, ed. C. Sammut, and G. Webb, 812–817. USA: Springer.Google Scholar
  25. Lalande, N., L. Berrahou, G. Molla, E. Serrano, F. Cernesson, C. Grac, A. Herrmann, F. Le Ber, M. Teisseire, and M. Trémolières. 2013. Feedbacks on data collection, data modeling and data integration of large datasets: application to Rhin-Meuse and Rhone-Mediterranean districts (France). In 8th Symposium for European Freshwater Sciences, Münster, Germany.Google Scholar
  26. Lalande, N., F. Cernesson, A. Decherf, and M.-G. Tournoud. 2014. Implementing the DPSIR framework to link water quality of rivers to land use: methodological issues and preliminary field test. International Journal of River Basin Management 1–17.Google Scholar
  27. Miralles, A., X. Dolques, M. Huchard, F. Le Ber, T. Libourel, C. Nebut, and A. Osman-Guédi. 2014. Exploration de la factorisation d’un modèle de classes sous contrôle des acteurs. In Inforsid 2014, Lyon, France.Google Scholar
  28. Saada, H., X. Dolques, M. Huchard, C. Nebut, and H.A. Sahraoui. 2012. Generation of operational transformation rules from examples of model transformations. In MoDELS, vol. 7590, Lecture Notes in Computer Science, France, eds. R.B. France, J. Kazmeier, R. Breu, and C. Atkinson, MoDELS, 546–561. Springer.Google Scholar
  29. Stumme, G., R. Taouil, Y. Bastide, N. Pasquier, and L. Lakhal. 2002. Computing iceberg concept lattices with Titanic. Data and Knowledge Engineering 42(2): 189–222.MATHCrossRefGoogle Scholar
  30. Valtchev, P., R. Missaoui, and R. Godin. 2004. Formal concept analysis for knowledge and data discovery: new challenges. In Proceedings of the 2nd International Conference on Formal Concept Analysis (ICFCA’04), 352–371.Google Scholar
  31. Vanderpoorten, A., J.-P. Klein, H. Stieperaere, and M. Trémolières. 1999. Variations of aquatic bryophyte assemblages in the Rhine Rift related to water quality. 1. The Alsatian Rhine floodplain. Journal of Bryology 21(1): 17–23.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Xavier Dolques
    • 1
  • 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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