Extracting Hierarchies of Closed Partially-Ordered Patterns Using Relational Concept Analysis

  • Cristina Nica
  • Agnès BraudEmail author
  • Xavier Dolques
  • Marianne Huchard
  • Florence Le Ber
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9717)


This paper presents a theoretical framework for exploring temporal data, using Relational Concept Analysis (RCA), in order to extract frequent sequential patterns that can be interpreted by domain experts. Our proposal is to transpose sequences within relational contexts, on which RCA can be applied. To help result analysis, we build closed partially-ordered patterns (cpo-patterns), that are synthetic and easy to read for experts. Each cpo-pattern is associated to a concept extent which is a set of temporal objects. Moreover, RCA allows to build hierarchies of cpo-patterns with two generalisation levels, regarding the structure of cpo-patterns and the items. The benefits of our approach are discussed with respect to pattern structures.


Relational Attribute Medical Examination Sequential Pattern Pattern Structure Concept Lattice 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Agrawal, R., Srikant, R.: Mining sequential patterns. In: International Conference on Data Engineering, pp. 3–14 (1995)Google Scholar
  2. 2.
    Arévalo, G., Falleri, J.-R., Huchard, M., Nebut, C.: Building abstractions in class models: formal concept analysis in a model-driven approach. In: Wang, J., Whittle, J., Harel, D., Reggio, G. (eds.) MoDELS 2006. LNCS, vol. 4199, pp. 513–527. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 3.
    Berrahou, L., Lalande, N., Serrano, E., Molla, G., Berti-Équille, L., Bimonte, S., Bringay, S., Cernesson, F., Grac, C., Ienco, D., Le Ber, F., Teisseire, M.: A quality-aware spatial data warehouse for querying hydroecological data. Comput. Geosci. Part A 85, 126–135 (2015)CrossRefGoogle Scholar
  4. 4.
    Buzmakov, A., Egho, E., Jay, N., Kuznetsov, S.O., Napoli, A., Raïssi, C.: On mining complex sequential data by means of FCA and pattern structures. Int. J. Gen. Syst. 45, 135–159 (2016)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Buzmakov, A., Egho, E., Jay, N., Kuznetsov, S.O., Napoli, A., Raïssi, C.: FCA and pattern structures for mining care trajectories. In: Proceedings of the International Workshop FCA4AI at IJCAI 2013. CEUR Workshop Proceedings, vol. 1058, pp. 7–14. (2013)Google Scholar
  6. 6.
    Casas-Garriga, G.: Summarizing sequential data with closed partial orders. In: 2005 SIAM International Conference on Data Mining, pp. 380–391 (2005)Google Scholar
  7. 7.
    Cheng, H., Yan, X., Han, J., Hsu, C.: Discriminative frequent pattern analysis for effective classification. In: International Conference on Data Engineering, pp. 716–725 (2007)Google Scholar
  8. 8.
    Codocedo-Henriquez, V.: Contributions to indexing and retrieval using formal concept analysis. Doctoral thesis, Université de Lorraine, September 2015Google Scholar
  9. 9.
    Dolques, X., Huchard, M., Nebut, C., Reitz, P.: Fixing generalization defects in UML use case diagrams. Fundam. Inform. 115(4), 327–356 (2012)zbMATHGoogle Scholar
  10. 10.
    Džeroski, S.: Relational data mining. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 869–898. Springer, New York (2005)CrossRefGoogle Scholar
  11. 11.
    Fabrègue, M., Braud, A., Bringay, S., Grac, C., Le Ber, F., Levet, D., Teisseire, M.: Discriminant temporal patterns for linking physico-chemistry and biology in hydro-ecosystem assessment. Ecol. Inform. 24, 210–221 (2014)CrossRefGoogle Scholar
  12. 12.
    Fabrègue, M., Braud, A., Bringay, S., Le Ber, F., Teisseire, M.: Mining closed partially ordered patterns, a new optimized algorithm. Knowl.-Based Syst. 79, 68–79 (2015)CrossRefGoogle Scholar
  13. 13.
    Ferré, S.: The efficient computation of complete and concise substring scales with suffix trees. In: Kuznetsov, S.O., Schmidt, S. (eds.) ICFCA 2007. LNCS (LNAI), vol. 4390, pp. 98–113. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  14. 14.
    Ganter, B., Kuznetsov, S.O.: Pattern structures and their projections. In: Delugach, H.S., Stumme, G. (eds.) ICCS 2001. LNCS (LNAI), vol. 2120, pp. 129–142. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  15. 15.
    Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations. Springer, Heidelberg (1999)CrossRefzbMATHGoogle Scholar
  16. 16.
    Kaytoue, M., Assaghir, Z., Messai, N., Napoli, A.: Two complementary classification methods for designing a concept lattice from interval data. In: Link, S., Prade, H. (eds.) FoIKS 2010. LNCS, vol. 5956, pp. 345–362. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  17. 17.
    Nica, C., Braud, A., Dolques, X., Huchard, M., Le Ber, F.: L’analyse relationnelle de concepts pour la fouille de données temporelles - Application à l’étude de données hydroécologiques. Revue des Nouvelles Technologies de l’Information Extraction et Gestion des Connaissances, EGC 2016, RNTI-E-30, pp. 267–278 (2016)Google Scholar
  18. 18.
    Poelmans, J., Elzinga, P., Viaene, S., Dedene, G.: A method based on temporal concept analysis for detecting and profiling human trafficking suspects. In: Artificial Intelligence and Applications, AIA 2010, pp. 1–9 (2010)Google Scholar
  19. 19.
    Rouane-Hacene, M., Huchard, M., Napoli, A., Valtchev, P.: Relational concept analysis: mining concept lattices from multi-relational data. Ann. Math. Artif. Intell. 67(1), 81–108 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Srikant, R., Agrawal, R.: Mining sequential patterns: generalizations and performance improvements. In: Apers, Peter M.G., Bouzeghoub, Mokrane, Gardarin, Georges (eds.) EDBT 1996. LNCS, vol. 1057, pp. 3–17. Springer, Heidelberg (1996)Google Scholar
  21. 21.
    Wang, M., Shang, X., Li, Z.: Sequential pattern mining for protein function prediction. In: Tang, C., Ling, C.X., Zhou, X., Cercone, N.J., Li, X. (eds.) ADMA 2008. LNCS (LNAI), vol. 5139, pp. 652–658. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  22. 22.
    Wolff, K.E.: Temporal concept analysis. In: ICCS 2001 Workshop on Concept Lattice for KDD, 9th International Conference on Conceptual Structures, pp. 91–107 (2001)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Cristina Nica
    • 1
  • Agnès Braud
    • 1
    Email author
  • Xavier Dolques
    • 1
  • Marianne Huchard
    • 2
  • Florence Le Ber
    • 1
  1. 1.ICube, University of Strasbourg, CNRS, ENGEESStrasbourgFrance
  2. 2.LIRMM, University of Montpellier, CNRSMontpellierFrance

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