Data Mining pp 27-47 | Cite as


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


Data Mining Relational Database Data Warehouse Data Mining Algorithm Structure Query Language 
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.
    Alzola, C., and Harrell, F. 1999. An introduction of S-Plus and the Hmisc and design libraries (download from Scholar
  2. 2.
    Ganti, V., Gehrke, J., and Ramakrishnan, R. Aug. 1999. Mining very large databases. IEEE Computer, 32(8):38–45Google Scholar
  3. 3.
    Holsheimer, M., and Siebes, A. 1994. Data Mining: The Search for Knowledge in Databases. Report CS-R9406, ISSN 0169–118X, CWI: Dutch National Research Center, Amersterdam, NetherlandsGoogle Scholar
  4. 4.
    Klosgen, W., and Zytkow, J. (Eds.). 2002. Handbook of Data Mining and Knowledge Discovery, Oxford University Press New York, USAGoogle Scholar
  5. 5.
    Lee, M-L., Ling, T.W., Lu, H., and Ko, Y.T. 1999. Cleansing data for mining and warehousing, Proceedings of the International Conference on Database and Expert Systems Applications (DEXA), Florence, Italy, Lecture Notes in Computer Science, 1677:751–760Google Scholar
  6. 6.
    Li, K-H. 1988. Imputation using markov chains. Journal of Statistical Computation and Simulation, 30:57–79zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Pipino, L., Lee, Y., and Wang, R. 2002. Data quality assessment. Communications of the ACM, 45(4):211–218CrossRefGoogle Scholar
  8. 8.
    Rubin, D.B. 1977. Formalizing subjective notions about the effect of nonrespondents in sample surveys. Journal of American Statistical Association, 72:538–543zbMATHCrossRefGoogle Scholar
  9. 9.
    Rubin, D.B. 1987. Multiple Imputations for Nonresponse in Surveys, John Wiley and Sons: New YorkGoogle Scholar
  10. 10.
    Rubin, D.B., and Schafer, J.L. 1990. Efficiently creating multiple imputation for incomplete multivariate normal data. Proceedings of the Statistical Computing Section, Alexandria: ASA, 83–8Google Scholar
  11. 11.
    Saul, J.M. 2000. Legal policy and security issues in the handling of medical data. In Cios, K.J. (Ed.), Medical Data Mining and Knowledge Discovery, Springer Verlag, 21–40Google Scholar
  12. 12.
    Shafer, J.L. 1997. Analysis of Incomplete Multivariate Data, Chapman and Hall Heidelberg, GermanyGoogle Scholar
  13. 13.
    Shafer, J.L. 1999. Multiple imputations: a primer. Statistical Methods in Medical Research, 8:3–15CrossRefGoogle Scholar
  14. 14.
    Wang, R., Storey, V., and Firth C. 1995. A framework for analysis of data quality research. IEEE Transactions on Knowledge and Data Engineering, 7(4):623–640CrossRefGoogle Scholar
  15. 15.
    Winter, R., and Auerbach, K. May 2004. Contents under pressure. Intelligent Enterprise, available online at http://allowbreak showArticle.jhtml?articleID=18902161Google Scholar
  16. 16.
    Zaïane, O., Han, J., Li, Z.N., and Hou, J. 1998. Mining multimedia data, Proceeding of the CASCON’98: Meeting of Minds, 83–96, Toronto, CanadaGoogle Scholar
  17. 17.
    Zhang, P. 2003. Multiple imputation: theory and method (with discussion). International Statistical Review, 71(3):581–592zbMATHCrossRefGoogle 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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