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The Knowledge Discovery Process

  • Krzysztof J. Cios
  • Roman W. Swiniarski
  • Witold Pedrycz
  • Lukasz A. Kurgan
Chapter

Keywords

Data Mining Knowledge Discovery Data Preparation Data Mining Tool Bibliographical Note 
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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References

  1. 1.
    Anand, S., and Buchner, A. 1998 Decision Support Using Data Mining. Financial Times Pitman Publishers, LondonGoogle Scholar
  2. 2.
    Anand, S., Hughes, P., and Bell, D. 1998 A data mining methodology for cross-sales. Knowledge Based Systems Journal, 10:449–461CrossRefGoogle Scholar
  3. 3.
    Brachman, R., and Anand, T. 1996 The process of knowledge discovery in databases: a human-centered approach. In Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., and Uthurusamy, R. (Eds.), Advances in Knowledge Discovery and Data Mining 37–58, AAAI PressGoogle Scholar
  4. 4.
    Cabena, P., Hadjinian, P., Stadler, R., Verhees, J., and Zanasi, A. 1998 Discovering Data Mining: From Concepts to Implementation, Prentice Hall Saddle River, New JerseyGoogle Scholar
  5. 5.
    Cios, K., Teresinska, A., Konieczna, S., Potocka, J., and Sharma, S. 2000 Diagnosing myocardial per-fusion from SPECT bull’s-eye maps a knowledge discovery approach. IEEE Engineering in Medicine and Biology Magazine, special issue on Medical Data Mining and Knowledge Discovery, 19(4):17–25}Google Scholar
  6. 6.
    Cios, K., and Kurgan, L. 2005 Trends in data mining and knowledge discovery. In Pal, N.R., and Jain L.C. (Eds.), Advanced Techniques in Knowledge Discovery and Data Mining, 1–26, Springer Verlag, London.Google Scholar
  7. 7.
    Fayyad, U., Piatesky-Shapiro, G., Smyth, P., and Uthurusamy, R. (Eds.), 1996. Advances in Knowledge Discovery and Data Mining, AAAI Press, CambridgeGoogle Scholar
  8. 8.
    Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. 1996 From data mining to knowledge discovery: an overview. In Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., and Uthurusamy, R. (Eds.), Advances in Knowledge Discovery and Data Mining, 1–34, AAAI Press, CambridgeGoogle Scholar
  9. 9.
    Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. 1996 The KDD process for extracting useful knowledge from volumes of data. Communications of the ACM, 39(11):27–34CrossRefGoogle Scholar
  10. 10.
    Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. 1996 Knowledge discovery and data mining: towards a unifying framework. Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, 82–88, Portland, OregonGoogle Scholar
  11. 11.
    Klosgen, W. 1992 Problems for knowledge discovery in databases and their treatment in the statistics interpreter explora. Journal of Intelligent Systems, 7(7):649–673CrossRefGoogle Scholar
  12. 12.
    Kurgan, L., Cios, K., Sontag, M., and Accurso, F. 2005 Mining the Cystic Fibrosis Data. In Zurada, J. and Kantardzic, M. (Eds.), Next Generation of Data-Mining Applications, 415–444, IEEE Press Piscataway, NJGoogle Scholar
  13. 13.
    Kurgan, L., and Musilek, P. 2006 A survey of knowledge discovery and data mining process models. Knowledge Engineering Review, 21(1):1–24CrossRefGoogle Scholar
  14. 14.
    Piatetsky-Shapiro, G. 1991 Knowledge discovery in real databases: a report on the IJCAI-89 workshop. AI Magazine, 11(5):68–70Google Scholar
  15. 15.
    Piatesky-Shapiro, G., and Matheus, C. 1992 Knowledge discovery workbench for exploring business databases. International Journal of Intelligent Agents, 7(7):675–686CrossRefGoogle Scholar
  16. 16.
    Piatesky-Shapiro, G. 1999 The data mining industry coming to age. IEEE Intelligent Systems, 14(6):32–33CrossRefGoogle Scholar
  17. 17.
    Shearer, C. 2000 The CRISP-DM model: the new blueprint for data mining. Journal of Data Warehousing, 5(4):13–19Google Scholar
  18. 18.
    Simoudis, E., Livezey, B., and Kerber, R. 1994 Integrating inductive and deductive reasoning for data mining. Proceedings of 1994 AAAI Workshop on Knowledge Discovery in Databases, 37–48, Seattle, Washington, USAGoogle Scholar
  19. 19.
    Wirth, R., and Hipp, J. 2000 CRISP-DM: towards a standard process model for data mining. Proceedings of the 4th International Conference on the Practical Applications of Knowledge Discovery and Data Mining, 29–39, Manchester, UKGoogle Scholar
  20. 20.
    Ziarko, R., Golan, R., and Edwards, D. 1993 An application of datalogic/R knowledge discovery tool to identify strong predictive rules in stock market data. Working notes from the Workshop on Knowledge Discovery in Databases, 89–101, Seattle, WashingtonGoogle Scholar
  21. 21.
    Zytow, J., and Baker, J. 1991 Interactive mining of regularities in databases. In Piatesky-Shapiro, G., and Frowley, W. (Eds.), Knowledge Discovery in Databases, 31–53, AAAI Press, Cambridge end thebibliography}Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Krzysztof J. Cios
    • 1
    • 2
  • Roman W. Swiniarski
    • 3
  • Witold Pedrycz
    • 4
  • Lukasz A. Kurgan
    • 5
  1. 1.Virginia Commonwealth University Computer Science DeptRichmond
  2. 2.University of ColoradoUSA
  3. 3.Computer Science DeptSan Diego State University & Polish Academy of SciencesSan DiegoUSA
  4. 4.Electrical and Computer Engineering DeptUniversity of AlbertaEdmontonCanada
  5. 5.Electrical and Computer Engineering DeptUniversity of AlbertaEdmontonCanada

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