Abstract

Using linguistic fuzzy variables to describe data improves the interpretability of data querying systems and thus their quality, under the condition that the considered modalities induce an indistinguishability relation in adequacy with the underlying data structure. This paper proposes a method to identify and split too general modalities so as to finally obtain a more appropriate vocabulary wrt. the data structure.

Keywords

interpretability indistinguishability linguistic variables adequacy 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Jain, A., Murty, M., Flynn, P.: Data clustering: a review. ACM Computing Survey 31(3), 264–323 (1999)CrossRefGoogle Scholar
  2. 2.
    Ruspini, E.H.: A new approach to clustering. Information and Control 15(1), 22–32 (1969)CrossRefMATHGoogle Scholar
  3. 3.
    Gacto, M.J., Alcalá, R., Herrera, F.: Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures. Information Sciences 181(20), 4340–4360 (2011)CrossRefGoogle Scholar
  4. 4.
    Marsala, C.: Fuzzy partition inference over a set of numerical values. In: Proc. of the IEEE Int. Conf. on Fuzzy Systems, pp. 1512–1517 (1995)Google Scholar
  5. 5.
    Guillaume, S., Charnomordic, B.: Generating an interpretable family of fuzzy partitions from data. IEEE Transactions on Fuzzy Systems 12(3), 324–335 (2004)CrossRefGoogle Scholar
  6. 6.
    Marsala, C.: Incremental tuning of fuzzy decision trees. In: Proc. of the 6th IEEE Int. Conf. on Soft Computing and Intelligent Systems, SCIS, pp. 2061–2064 (2012)Google Scholar
  7. 7.
    Meila, M.: Comparing clustering, an axiomatic view. In: Proc. of the 22nd Int. Conf. on Machine Learning (2005)Google Scholar
  8. 8.
    Le Capitaine, H., Frélicot, C.: A cluster-validity index combining an overlap measure and a separation measure based on fuzzy-aggregation operators. IEEE Trans. on Fuzzy Systems 19(3), 580–588 (2011)CrossRefGoogle Scholar
  9. 9.
    Valet, L., Mauris, G., Bolon, P., Keskes, N.: A fuzzy linguistic-based software tool for seismic image interpretation. IEEE Trans. on Instrumentation and Measurement 52(3), 675–680 (2003)CrossRefGoogle Scholar
  10. 10.
    Raschia, G., Mouaddib, N.: Évaluation de la qualité des partitions de concepts dans un processus de résumés de bases de données. In: Proc. of LFA (2000)Google Scholar
  11. 11.
    Lesot, M.J., Smits, G., Pivert, O.: Adequacy of a user-defined vocabulary to the data structure. In: Proc. of the IEEE Int. Conf. on Fuzzy Systems (2013)Google Scholar
  12. 12.
    Lesot, M.-J., Revault d’Allonnes, A.: Credit-card fraud profiling using a hybrid incremental clustering methodology. In: Hüllermeier, E., Link, S., Fober, T., Seeger, B. (eds.) SUM 2012. LNCS, vol. 7520, pp. 325–336. Springer, Heidelberg (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Grégory Smits
    • 1
  • Olivier Pivert
    • 1
  • Marie-Jeanne Lesot
    • 2
    • 3
  1. 1.IRISA, UMR 6074University of Rennes 1LannionFrance
  2. 2.UMR 7606, LIP6Sorbonne Universités, UPMC Univ Paris 06ParisFrance
  3. 3.CNRS, UMR 7606, LIP6ParisFrance

Personalised recommendations