A Comparative Study of a New Associative Classification Approach for Mining Rare and Frequent Classification Rules

  • Ines Bouzouita
  • Michel Liquiere
  • Samir Elloumi
  • Ali Jaoua
Part of the Communications in Computer and Information Science book series (CCIS, volume 200)


In this paper, we tackled the problem of generation of rare classification rules. Our work is motivated by the search of an effective algorithm allowing the extraction of rare classification rules by avoiding the generation of a large number of patterns at reduced time. Within this framework we are interested in rules of the form a 1 ∧ a 2… ∧ a n b which allow us to propose a new approach based on genetic algorithms principle. This approach allows obtaining frequent and rare rules while avoiding making a breadth search. We describe our method and provide a comparative study of three versions of our method on standard benchmark data sets.


Cover Set Associative Classification Rare Classification Rules 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Zaiane, O., Antonie, M.: On pruning and tuning rules for associative classifiers. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds.) KES 2005. LNCS (LNAI), vol. 3683, pp. 966–973. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  2. 2.
    Xiaoxin Yin, J.H.: CPAR: Classification based on Predictive Association Rules. In: Proceedings of the SDM, San Francisco, CA, pp. 369–376 (2003)Google Scholar
  3. 3.
    Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. Knowledge Discovery and Data Mining, 80–86 (1998)Google Scholar
  4. 4.
    Antonie, M., Zaiane, O.: Text Document Categorization by Term Association. In: Proceedings of the IEEE International Conference on Data Mining (ICDM 2002), Maebashi City, Japan, pp. 19–26 (2002)Google Scholar
  5. 5.
    Antonie, M., Zaiane, O.: Classifying Text Documents by Associating Terms with Text Categories. In: Proceedings of the Thirteenth Austral-Asian Database Conference (ADC 2002), Melbourne, Australia (2002)Google Scholar
  6. 6.
    Bouzouita, I., Elloumi, S., Yahia, S.B.: GARC: A new associative classification approach. In: Tjoa, A.M., Trujillo, J. (eds.) DaWaK 2006. LNCS, vol. 4081, pp. 554–565. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Bouzouita, I., Elloumi, S.: Integrated generic association rules based classifier. In: Wagner, R., Revell, N., Pernul, G. (eds.) DEXA 2007. LNCS, vol. 4653, pp. 514–518. Springer, Heidelberg (2007)Google Scholar
  8. 8.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) Proceedings of the 20th Intl. Conference on Very Large Databases, Santiago, Chile, pp. 478–499 (1994)Google Scholar
  9. 9.
    Li, W., Han, J., Pei, J.: CMAR: Accurate and efficient classification based on multiple class-association rules. In: Proceedings of IEEE International Conference on Data Mining (ICDM 2001), pp. 369–376. IEEE Computer Society, San Jose (2001)Google Scholar
  10. 10.
    Wang, J., Karypis, G.: HARMONY: Efficiently mining the best rules for classification. In: Proceedings of the International Conference of Data Mining, pp. 205–216 (2005)Google Scholar
  11. 11.
    Quinlan, J., Cameron-Jones, R.: FOIL: A midterm report. In: Proceedings of European Conference on Machine Learning, Vienna, Austria, pp. 3–20 (1993)Google Scholar
  12. 12.
    Bouzouita, I., Michel Liquire, S.E.: Afortiori: an associative classification approach based on covering set method. In: International Conference Of Formal Concept Analysis ICFCA 2009. Darmstadt University of Applied Sciences, Germany (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ines Bouzouita
    • 1
    • 2
  • Michel Liquiere
    • 2
  • Samir Elloumi
    • 1
  • Ali Jaoua
    • 1
    • 3
  1. 1.Computer Science DepartmentFaculty of Sciences of TunisTunisTunisia
  2. 2.LIRMMMontpellier, Cedex 5Tunisia
  3. 3.Qatar UniversityTunisia

Personalised recommendations