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DBROUGH: A rough set based knowledge discovery system

  • Xiaohua Hu
  • Ning Shan
  • Nick Cercone
  • Wojciech Ziarko
Communications Learning and Adaptive Systems
Part of the Lecture Notes in Computer Science book series (LNCS, volume 869)

Abstract

Knowledge discovery in databases, or data mining, is an important objective in the development of data- and knowledge-base systems. An attribute-oriented rough set method is developed for knowledge discovery in databases. The method integrates learning from example techniques with rough set theory. An attribute-oriented concept tree ascension technique is first applied in generalization, which substantially reduces the computational complexity of the database learning processes. Then the rough set techniques are applied to the generalized relation to derive different knowledge rules. Moreover, the approach can find all the maximal generalized rules in the data. Based on these principles, a prototype database learning system, DBROUGH, has been constructed. Our study shows that attribute-oriented induction combined with rough set techniques provide an efficient and effective mechanism for knowledge discovery in database systems.

Keywords kwMachine Learning

Knowledge Discovery in Databases Methodologies 

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References

  1. 1.
    Y. Cai, N. Cercone and J. Han, Attribute-Oriented Induction in Relational databases, Knowledge Discovery in Database, AAAI/MIT Press, G.Piatetsky-Shapiro and W.J. Frawley (eds) pp. 213–228, 1991.Google Scholar
  2. 2.
    T.G. Dietterich and R.S. Michalski, A Theory and Methodology of Inductive Learning, in Machine Learning: An Artificial Intelligence Approach, Vol. 1. Michalski et. al. (eds), Morgan Kaufmann, pp 43–82, 1983.Google Scholar
  3. 3.
    W. J. Frawley, G. Piatetsky and C.J. Matheus, Knowledge Discovery in Database: An Overview, Knowledge Discovery in Database, AAAI/MIT Press, G.Piatetsky-Shapiro and W.J. Frawley (eds), pp. 1–27, 1991.Google Scholar
  4. 4.
    Wojciech Ziarko, The Discovery, Analysis, and Representation of Data Dependencies in Databases, in Knowledge Discovery in Databases G. Piatetsky-Shapiro and W. J. Frawlwy, (eds) Menlo Park, CA: AAAI/MIT, 1990, 213–228Google Scholar
  5. 5.
    J. Han, Y.Cai, N. Cercone, Knowledge Discovery in Databases: An AttributeOriented Approach, Proceeding of the 18th VLDB Conference, Vancouver, B.C., Canada, pp 340–355, 1992Google Scholar
  6. 6.
    X. Hu, N. Cercone, J. Han, A Rough Set Approach for Knowledge Discovery in Databases, The International Workshop on Rough Set and Knowledge Discovery, Banff, Alberta, Canada, October 12–15, 1993. pp79–94Google Scholar
  7. 7.
    R.S. Mickalski, A Theory and Methodology of Inductive Learning, in Machine Learning: An Artificial Intelligence Approach, Vol. 1. Michalski et. al. (eds), Morgan Kaufmann, 1983, pp 41–82.Google Scholar
  8. 8.
    Zdzislaw Pawlak, Rough sets, International Journal of Information and Computer Science (1982) 11(5), 341–356Google Scholar
  9. 9.
    Zdzislaw Pawlak, Rough Classification, International Journal of Man-Machine Studies (1984) 20, 469–483Google Scholar
  10. 10.
    A. Silberschatz, M. Stonebraker and J.D. Ullman, Database Systems:Achievements and Opportunities, Comm. ACM, 34(10), 1991, pp. 94–109.Google Scholar
  11. 11.
    A. Skowron, C. Rauszer, The discernibility matrices and functions in information systems. ICS Research Report 1/91, Wawsaw University of Technology, Nowowiejska 15/19, 00-665, Warsaw, PolandGoogle Scholar
  12. 12.
    Wojciech Ziarko, Ning Shan, A Rough Set-Based Method for Computing All Minimal Determintic Rules on Attribute-Value Systems, Technical Report CS-93-02 Dept. of Computer Science, University of Regina, CanadaGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  • Xiaohua Hu
    • 1
  • Ning Shan
    • 1
  • Nick Cercone
    • 1
  • Wojciech Ziarko
    • 1
  1. 1.Department of Computer ScienceUniversity of ReginaReginaCanada

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