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Exploring Faulty Data

  • Daniel Borchmann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9113)

Abstract

Within formal concept analysis, attribute exploration is a powerful tool to semi-automatically check data for completeness with respect to a given domain. However, the classical formulation of attribute exploration does not take into account possible errors which are present in the initial data. To remedy this, we present in this work a generalization of attribute exploration based on the notion of confidence, that will allow for the exploration of implications which are not necessarily valid in the initial data, but instead enjoy a minimal confidence therein.

Notes

Acknowledgments

This work has been partially supported by the DFG Research Training Group 1763 “QuantLA”, and by the Cluster of Excellence “Center for Advancing Electronics Dresden” (cfAED). Additionally, the author is grateful to the anonymous reviewers for the detailed and helpful comments.

References

  1. 1.
    Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 207–216 (1993)Google Scholar
  2. 2.
    Baader, F., et al. (eds.): The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press, New York (2003)Google Scholar
  3. 3.
    Borchmann, D.: A general form of attribute exploration. LTCS-Report 13–02. Chair of Automata Theory, Institute of Theoretical Computer Science, Technische Universität Dresden (2013)Google Scholar
  4. 4.
    Borchmann, D.: Learning terminological knowledge with high confidence from erroneous data. Ph.D. thesis, Technische Universität Dresden (2014)Google Scholar
  5. 5.
    Burmeister, P., Holzer, R.: Treating incomplete knowledge in formal concept analysis. In: Ganter, B., Stumme, G., Wille, R. (eds.) Formal Concept Analysis. LNCS (LNAI), vol. 3626, pp. 114–126. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  6. 6.
    Distel, F.: Learning description logic knowledge bases from data using methods from formal concept analysis. Ph.D. thesis, Technische Universität Dresden (2011)Google Scholar
  7. 7.
    Ganter, B.: Attribute exploration with background knowledge. Theor. Comput. Sci. 217(2), 215–233 (1999)CrossRefzbMATHMathSciNetGoogle Scholar
  8. 8.
    Ganter, B.: Two basic algorithms in concept analysis. In: Kwuida, L., Sertkaya, B. (eds.) ICFCA 2010. LNCS, vol. 5986, pp. 312–340. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  9. 9.
    Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations. Springer, Heidelberg (1999)CrossRefzbMATHGoogle Scholar
  10. 10.
    Luxenburger, M.: Implikationen, Abhängigkeiten und Galois-Abbildungen. Ph.D. thesis, TH Darmstadt (1993)Google Scholar
  11. 11.
    Stumme, G.: Attribute exploration with background implications and exceptions. In: Bock, H.-H., Polasek, W. (eds.) Data Analysis and Information Systems: Studies in Classification, Data Analysis, and Knowledge Organization, pp. 457–469. Springer, Heidelberg (1996)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Institute of Theoretical Computer ScienceTU DresdenDresdenGermany
  2. 2.Center for Advancing Electronics DresdenDresdenGermany

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