Nowadays there is a huge amount of data stored in real-world databases, and this amount continues to grow fast. As pointed out by [Piatetsky-Shapiro 1991], this creates both an opportunity and a need for (semi-)automatic methods that discover the knowledge “hidden” in such databases. If such knowledge discovery activity is successful, discovered knowledge can be used to improve the decision-making process of an organization.


Data Mining Evolutionary Algorithm Knowledge Representation Candidate Solution Prediction Rule 
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. [Dhar and Stein 1997]
    V. Dhar and R. Stein. Seven Methods for Transforming Corporate Data into Business Intelligence. Prentice-Hall, 1997.Google Scholar
  2. [Dhar et al. 2000]
    V. Dhar, D. Chou and F. Provost. Discovering interesting patterns for investment decision making with GLOWER — a genetic learner overlaid with entropy reduction. Data Mining and Knowledge Discovery 4 (4), 251–280, 2000.CrossRefMATHGoogle Scholar
  3. [Fayyad et al. 1996]
    U.M. Fayyad, G. Piatetsky-Shapiro and P. Smyth. From data mining to knowledge discovery: an overview. In: U.M. Fayyad, G. Piatetsky Shapiro, P. Smyth and R. Uthurusamy (Eds.) Advances in Knowledge Discovery and Data Mining, 1–34. AAAI/MIT Press, 1996.Google Scholar
  4. [Frawley et al. 1991]
    W.J. Frawley, G. Piatetsky-Shapiro and C.J. Matheus. Knowledge discovery in databases: an overview. In: G. Piatetsky-Shapiro and W.J. Frawley (Eds.) Knowledge Discovery in Databases, 1–27. AAAI/MIT Press, 1991.Google Scholar
  5. [Freitas 2002a]
    A.A. Freitas. Evolutionary algorithms. To appear in: J. Zytkow and W. Klosgen (Eds.) Handbook of Data Mining and Knowledge Discovery. Oxford University Press, 2002.Google Scholar
  6. [Freitas 2002b]
    A.A. Freitas. A survey of evolutionary algorithms for data mining and knowledge discovery. To appear in: A. Ghosh and S. Tsutsui (Eds.) Advances in Evolutionary Computing. Springer, 2002.Google Scholar
  7. [Gupta et al. 1999]
    A. Gupta, S. Park and S.M. Lam. Generalized analytic rule extraction for feedforward neural networks. IEEE Transactions on Knowledge and Data Engineering, 11 (6), 985–991, 1999.CrossRefGoogle Scholar
  8. [Greene and Smith 1993]
    D.P. Greene and S.F. Smith. Competition-based induction of decision models from examples. Machine Learning 13, 229–257. 1993.CrossRefGoogle Scholar
  9. [Henery 1994]
    R.J. Henery. Classification. In: D. Michie, D.J. Spiegelhalter and C.C. Taylor. Machine Learning, Neural and Statistical Classification, 6–16. Ellis Horwood, 1994.Google Scholar
  10. [Holsheimer and Siebes 1994]
    M. Holsheimer and A. Siebes. Data mining: the search for knowledge in databases. Report CS-R9406 CWI, Amsterdam, Jan. 1994.Google Scholar
  11. [Langley 1996]
    P. Langley. Elements of Machine Learning. Morgan Kaufmann, 1996.Google Scholar
  12. [Lavrac and Dzeroski 1994]
    N. Lavrac and S. Dzeroski. Inductive Logic Programming: Techniques and Applications. Ellis Horwood, 1994.Google Scholar
  13. [Michalski 1983]
    R. Michalski. A theory and methodology of inductive learning. Artificial Intelligence 20, 111–161, 1983.MathSciNetCrossRefGoogle Scholar
  14. [Michie et al. 1994]
    D. Michie, D.J. Spiegelhalter and C.C. Taylor. Machine Learning, Neural and Statistical Classification. Ellis Horwood, 1994.Google Scholar
  15. [Mitchell 1997]
    T. Mitchell. Machine Learning. McGraw-Hill, 1997.Google Scholar
  16. [Piatetsky-Shapiro 1991]
    G. Piatetsky-Shapiro. Knowledge discovery in real databases: a report on the IJCAI’ 89 Workshop. AI Magazine 11 (5), 68–70, 1991.Google Scholar
  17. Santos et al. 2000] R. Santos, J.C. Nievola and A.A. Freitas. Extracting comprehensible rules from neural networks via genetic algorithms. Proceedings of the 2000 IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks (ECNN’ 2000), 130–139. San Antonio, TX, USA. May 2000.Google Scholar
  18. [Taha and Ghosh 1999]
    I.A. Taha and J. Ghosh. Symbolic interpretation of artificial neural networks. IEEE Transactions on Knowledge and Data Engineering 11 (3), 448–463, 1999.CrossRefGoogle Scholar
  19. [Witten and Frank 2000]
    I.H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, 2000.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Alex A. Freitas
    • 1
  1. 1.Computing LaboratoryUniversity of KentCanterburyUK

Personalised recommendations