ES-DM: An Expert System for an Intelligent Exploitation of the Large Data Set

  • Amel Grissa Touzi
  • Mohamed Amine Selmi
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 16)


In meeting the challenges that resulted from the explosion of collected, stored, and transferred data, Knowledge Discovery in Databases (KDD) or Data Mining has emerged as an important research area. However, the approaches studied in this area have mainly been oriented at highly structured and precise data. Thus, the problem of exploit these data is often neglected. In this paper, we propose an intelligent approach for exploitation of these data. For this, we propose to define an Expert System (ES) allowing the user to easily exploit the large data set. The Knowledge Base (KB) of our ES is defined by introducing a new KDD approach taking in consideration another degree of granularity into the process of knowledge extraction. This set represents a reduced knowledge of the initial data set and allows deducting the semantics of the data. We prove that, this ES can help the user to give semantics for these data and to exploit them in intelligent way.


Expert System Association Rule Fuzzy Cluster Inference Engine Formal Concept Analysis 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Goebel, M., Gruenwald, L.: A Survey of Data Mining and Knowledge Discovery Software Tools. In: SIGKDD, ACM SIGKDD, vol. 1(1), pp. 20–33 (June 1999)Google Scholar
  2. 2.
    Zaki, M.: Mining Non-Redundant Association Rules. Data Mining and Knowledge Discovery (9), 223–248 (2004)Google Scholar
  3. 3.
    Stumme, G., Taouil, R., Bastide, Y., Pasquier, N., Lakhal, L.: Intelligent Structuring and Reducing of Association Rules with Formal Concept Analysis. In: Baader, F., Brewka, G., Eiter, T. (eds.) KI 2001. LNCS (LNAI), vol. 2174, pp. 335–350. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  4. 4.
    Billard, L., Diday, E.: Symbolic Data Analysis: Conceptual Statistics and Data Mining. Wiley (2007)Google Scholar
  5. 5.
    Sato-Ilic, M.: Symbolic Clustering with Interval-Valued Data, Complex Adaptive Systems. Procedia Computer Sciences 6, 358–363 (2011)CrossRefGoogle Scholar
  6. 6.
    Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules between sets of items in large Databases. In: Proceedings of the ACM SIGMOD Intl. Conference on Management of Data, Washington, USA, pp. 207–216 (June 1993)Google Scholar
  7. 7.
    Agrawal, R., Skirant, R.: Fast algoritms for mining association rules. In: Proceedings of the 20th Int’l Conference on Very Large Databases, pp. 478–499 (June 1994)Google Scholar
  8. 8.
    Ganter, B., Wille, R.: Formal Concept Analysis: mathematical foundations (translated from the German by Cornelia Franzke). Springer, Heidelberg (1999)Google Scholar
  9. 9.
    Thanh, T., Siu Cheung, H., Tru Hoang, C.: A Fuzzy FCA-based Approach to Conceptual Clustering for Automatic Generation of Concept Hierarchy on Uncertainty Data. In: CLA 2004, pp. 1–12 (2004) ISBN 80-248-0597-9Google Scholar
  10. 10.
    Grissa Touzi, A., Sassi, M., Ounelli, H.: An innovative contribution to flexible query through the fusion of conceptual clustering, fuzzy logic, and formal concept analysis. International Journal of Computers and Their Applications 16(4), 220–233 (2009)Google Scholar
  11. 11.
    Sun, H., Wang, S., Jiang, Q.: FCM-Based Model Selection Algorithms for Determining the Number of Clusters. Pattern Recognition 37, 2027–2037 (2004)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Ecole Nationale d’Ingénieurs de TunisUniversité de Tunis El ManarTunisTunisia

Personalised recommendations