Rule Induction Via Clustering Decision Classes

  • Yoshifumi Kusunoki
  • Masahiro Inuiguchi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4259)


In this paper, we examine the effects of the application of LEM2 to a hierarchical structure of decision classes. We consider classification problems with multiple decision classes by nominal condition attributes. To such a problem, we first apply an agglomerative hierarchical clustering method to obtain a dendrogram of decision classes, i.e., a hierarchical structure of decision classes. At each branch of the dendrogram, we then apply LEM2 to induce rules inferring a cluster to which an object belongs. A classification system suitable for the proposed rule induction method is designed. By a numerical experiment, we compare the proposed methods with different similarity measure calculations, the standard application of LEM2 and a method with randomly generated dendrogram. As the result, we generally demonstrate the advantages of the proposed method.


Decision Table Rule Induction Standard Application Matching Rule Decision Class 
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.
    Bazan, J.G., Nguyen, H.S., Nguyen, S.H., Synak, P., Wróblewski, J.: Rough Set Algorithm in Classification Problem. In: Polkowski, L., Tsumoto, S., Lin, T.Y. (eds.) Rough Set Methods and Applications, pp. 49–88. Physica-Verlag, Heidelberg (2000)Google Scholar
  2. 2.
    Grzymala-Busse, J.W.: LERS – A system for learning from examples based on rough sets. In: Słowiński, R. (ed.) Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory, pp. 3–18. Kluwer Academic Publishers, Dordrecht (1992)Google Scholar
  3. 3.
    Grzymala-Busse, J.W., Stefanowski, J.: Three Discretization Methods for Rule Induction. International Journal of Intelligent Systems 16, 29–38 (2001)MATHCrossRefGoogle Scholar
  4. 4.
    Jelonek, J., Stefanowski, J.: Experiments on solving multiclass learning problems by n 2-classifier. In: Proc. AI-METH 2002, Gliwice, pp. 297–301 (2002)Google Scholar
  5. 5.
    Kim, B., Landgrebe, D.A.: Hierarchical classifier design in high-dimensional numerous class cases. IEEE Trans. Geoscience and Remote Sensing 29(4), 518–528 (1991)CrossRefGoogle Scholar
  6. 6.
    Kumar, S., Ghosh, J., Crawford, M.M.: Hierarchical fusion of multiple classifiers for hyperspectral data analysis. Pattern Analysis & Applications 5, 210–220 (2002)MATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Miyamoto, S.: Introduction to Cluster Analysis (in Japanese). Morikita, Tokyo (1999)Google Scholar
  8. 8.
    Newman, D.J., Hettich, S., Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases (1998),
  9. 9.
    Pawlak, Z.: Rough sets. Int. J. Inform. Comp. Sci. 11(5), 341–356 (1982)MATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Polkowski, L.: Concerning granular computing based on Archimedean rough inclusion. In: Proc. IPMU 2004, CD-ROM (July 2004)Google Scholar
  11. 11.
    Rokach, L., Maimon, O.: Clustering methods. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 321–352. Springer, New York (2005)CrossRefGoogle Scholar
  12. 12.
    Stefanowski, J.: The bagging and n 2-classifiers based on rules induced by MODLEM. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds.) RSCTC 2004. LNCS (LNAI), vol. 3066, pp. 488–497. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  13. 13.
    Tsumoto, S.: Automated extraction of hierarchical decision rules from clinical databases using rough set model. Expert Systems with Applications 24, 189–197 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yoshifumi Kusunoki
    • 1
  • Masahiro Inuiguchi
    • 1
  1. 1.Department of Systems Innovation, Graduate School of Engineering ScienceOsaka UniversityToyonaka, OsakaJapan

Personalised recommendations