From Clusters to Rules: A Hybrid Framework for Generalized Symbolic Rule Induction

  • Qingshuang Jiang
  • Syed Sibte Raza Abidi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3930)


Rule induction is a data mining process for acquiring knowledge in terms of symbolic decision rules that explain the data in terms of causal relationship between conditional factors and a given decision/outcome. We present a Decision Rule Acquisition Workbench (DRAW) that discovers symbolic decision rules, in CNF form, from un-annotated data-sets. Our rule-induction strategy involves three phases: (a) conceptual clustering to cluster and generate a conceptual hierarchy of the data-set; (b) rough sets based rule induction algorithm to generate decision rules from the emergent data clusters; and (c) attribute oriented induction to generalize the derived decision rules to yield high-level decision rules and a minimal rule-set size. We evaluate DRAW with five standard machine learning datasets and apply to derive decision rules to understand optic nerve images in the realm of glaucoma decision support.


Decision Rule Conjunctive Normal Form Rule Induction Conceptual Hierarchy Conceptual Cluster 
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.


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  1. 1.
    Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P.: Knowledge Discovery and Data Mining: Towards a Unifying Framework. In: International Conference on Knowledge Discovery in Databases, pp. 82–88 (1996)Google Scholar
  2. 2.
    Abidi, S.S.R., Hoe, K.M., Goh, A.: Analyzing Data Clusters: A Rough Set Approach to Extract Cluster Defining Symbolic Rules. In: Hoffmann, F., Adams, N., Fisher, D., Guimarães, G., Hand, D.J. (eds.) IDA 2001. LNCS, vol. 2189, pp. 248–257. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  3. 3.
    Pawlak, Z.: Rough Sets. In: Lin, T.Y., Cercone, N. (eds.) Rough Sets and Data Mining: Analysis of Imprecise Data, pp. 3–7. Kluwer Academic Publishers, Dordrecht (1997)Google Scholar
  4. 4.
    Biswas, G., Weinberg, J., Li, C.: ITERATE: A Conceptual Clustering Method for Knowledge Discovery in Database. In: Braunshweig, B., Day, R. (eds.) Artificial Intelligence in the Petroleum Industry, ch. 3, Techniq edn., pp. 111–139 (1995)Google Scholar
  5. 5.
    Slowinski, K., Slowinski, R., Stefanowski, J.: Rough Sets Approach to Analysis of Data from Peritoneal Lavage in Acute Pancreatitis. Medical Informatics 13, 143–159 (1988)CrossRefGoogle Scholar
  6. 6.
    Han, J., Cai, Y., Cercone, N.: Knowledge Discovery in Databases: An Attribute-Oriented Approach. In: Proceedings of the 18th VLDB Conference, Vancouver, British Columbia, Canada, pp. 547–559 (1992)Google Scholar
  7. 7.
    Shavlik, J.W., Dietterich, T.G.: Readings in Machine learning. Morgan Kaufmann, San Francisco (1990)Google Scholar
  8. 8.
    Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, California (1993)Google Scholar
  9. 9.
    Hu, X., Cercone, N.: Learning Maximal Generalized Decision Rules via Discretization, Generalization and Rough Set Feature Selection. In: Proc. 9th Int. Conf. on Tools with Artificial Intelligence (1997)Google Scholar
  10. 10.
    Grzymala Busse, J.W.: LERS A System for Learning from Examples Based on Rough Sets. In: Slowinski, R. (ed.) Intelligent Decision Support, pp. 3–18. Kluwer Publishers, Dordrecht (1992)Google Scholar
  11. 11.
    Chan, C.C., Grzymala Busse, J.W.: On the Two Local Inductive Algorithms: PRISM and LEM2. Foundations of Computing and Decision Sciences 19(4), 185–204 (1994)MATHGoogle Scholar
  12. 12.
    Skowron, A., Polkowski, L.: Synthesis of Decision Systems from Data Tables. In: Lin, T.Y., Cecrone, N. (eds.) Rough Sets and Data Mining, pp. 289–299. Kluwer Publishers, Dordrecht (1997)Google Scholar
  13. 13.
    Ohrn, A., Komorowski, J.: Diagnosing Acute Appendicitis with Very Simple Classification Rules. In: Żytkow, J.M., Rauch, J. (eds.) PKDD 1999. LNCS (LNAI), vol. 1704, pp. 462–467. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  14. 14.
    Goodall, D.W.: A New Similarity Index Based on Probability. Biometrics 22, 882–907 (1966)CrossRefGoogle Scholar
  15. 15.
    Arabie, P., Hubert, L.J.: An Overview of Combinatorial Data Analysis. In: Arabie, P., Hubert, L.J., Soete, G.D. (eds.) Clustering and Classification, pp. 5–63. World Scientific Publishing Co, NJ (1996)Google Scholar
  16. 16.
    Bazan, J.G.: Dynamic Reducts and Statistical Inference. In: Proc. 6th Int. Conf. on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMIU 1996), Granada, Spain, vol. 3, pp. 1147–1152 (1996)Google Scholar
  17. 17.
    Wróblewski, J.: Finding Minimal Reducts using Genetic Algorithms. In: Proc. 2nd Annual Joint Conf. on Information Sciences, Wrightsville Beach, NC, USA, pp. 186–189 (1995)Google Scholar
  18. 18.
    Komorowski, J., Bjorvand, A.T.: Practical Applications of Genetic Algorithms for Efficient Reduct Computation. In: Proc. of 15th IMACS World Congress on Scientific Computation, Modelling and Applied Mathematics, Berlin (1997)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Qingshuang Jiang
    • 1
  • Syed Sibte Raza Abidi
    • 1
  1. 1.Faculty of Computer ScienceDalhousie UniversityHalifaxCanada

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