Skip to main content
SpringerLink
Log in

A Survey of Data Mining Techniques

  • Conference paper
  • First Online:
Medical Data Analysis (ISMDA 2000)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1933))

Included in the following conference series:

Abstract

In this short paper we have resumed a keynote speech, to be given at the ISMDA 2000 conference, about data mining research and tools. We state a brief summary of the main concepts associated to data mining and some of the methods and tools used in the scientific world, mainly those that can associated to medical applications. Finally, some practical projects and conclusions are presented.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Selected Bibliography

  1. Ivan Bratko and Stephen Muggleton. Applications of inductive logic programming. Communications of the ACM, 38(11):65–70, November 1995.

    Article  Google Scholar 

  2. L. Breiman, J. Friedman, R. Olshen, and Stone C. Classification and Regression Trees. Wadsworth International Group, 1984.

    Google Scholar 

  3. Carla E. Brodley. Recursive automatic bias selection for classifier construction. Machine Learning, 20:63–94, 1995.

    Google Scholar 

  4. P. Clark and R. Boswell. Rule induction with CN2: Some recent improvements. In Y. Kodratoff, editor, Proceedings of the European Working Session on Learning: Machine Learning (EWSL-91), volume 482 of LNAI, pages 151–163, Porto, Portugal, March 1991. Springer Verlag.

    Google Scholar 

  5. Peter Clark and Tim Niblett. The CN2 induction algorithm. Machine Learning, 3:261, 1988.

    Google Scholar 

  6. Usama M. Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth. Advances in Knowledge Discovery and Data Mining, chapter From Data Mining to Knowledge Discovery: An Overview. The MIT Press, March 1996.

    Google Scholar 

  7. W.J. Frawley, G., Piatetsky-Shapiro, and C.J. Matheus. Knowledge discovery in databases: an overview. AI Magazine, 13(3):57–70, 1992.

    Google Scholar 

  8. Michael Goebel and Le Gruenwald. A survey of data mining and knowledge discovery software tools. In SIGKDD Explorations. ACM SIGKDD, June 1999.

    Google Scholar 

  9. John H. Holland. Escaping brittleness. In Proceedings Second International Workshop on Machine Learning, pages 92–95, 1983.

    Google Scholar 

  10. John H. Holland, J. Holyoak K, R. E. Nisbett, and P. R. Thagard. Induction: Processes of Inference, Learning, and Discovery. MIT Press, Cambridge, MA, 1987.

    Google Scholar 

  11. Pat Langley and H.A. Simon. Applications of machine learning and rule induction. Communications of the ACM, 38(11):55–64, Nov 1995.

    Article  Google Scholar 

  12. Nada Lavrac, Elpida Keravnou, and Blaz Zupan. Intelligent Data Analysis in Medicine and Pharmacology, chapter Intelligent Data Analysis in Medicine and Pharmacology: An Overview, pages 1–13. Kluwer, 1997.

    Google Scholar 

  13. T.-S. Lim, W.-Y. Loh, and Y.-S. Shih. An empirical comparison of decision trees and other classification methods. Technical Report 979, Department of Statistics, University of Wisconsin-Madison, Madison, WI, June 30 1997.

    Google Scholar 

  14. W.J. Long, J.L. Griffith, H.P. Selker, and R.B D’Agostino. A comparison of logistic regression to decision-tree induction in a medical domain. Computers and Biomedical Research, 26:74–97, 1993.

    Article  Google Scholar 

  15. R. Michalski, I. Mozetic, J. Hong, and N. Lavrac. The AQ15 inductive learning system: an overview and experiments. In Proceedings of IMAL 1986, Orsay, 1986. Université de Paris-Sud.

    Google Scholar 

  16. R S Michalski. A theory and methodology of inductive learning. Artificial Intelligence, 20:111–161, 1983.

    Article  MathSciNet  Google Scholar 

  17. Ryszard S. Michalski, Igor Mozetic, Jiarong Hong, and Nada Lavrac. The multi-purpose incremental learning system AQ15 and its testing application to three medical domains. In Proceedings of the 5th national conference on Artificial Intelligence, pages 1041–1045, Philadelphia, 1986.

    Google Scholar 

  18. J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA, 1992.

    Google Scholar 

  19. Weiss, S and Indurkhya:. Predictive Data Mining. A Practical Guide.Morgan Kaufmann. San Francisco, CA. 1998.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Medical Informatics Group. Artificial Intelligence Laboratory. School of Computer Science, Universidad Politecnica of Madrid, Boadilla del Monte, 28660, Madrid, Spain

    Victor Maojo & José Sanandrés

Authors
  1. Victor Maojo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. José Sanandrés
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Fachbereich Biologie und Informatik AG Adaptive Systemarchitektur, Johann Wolfgang Goethe-Universität, Robert-Mayer-Str. 11-15, 60054, Frankfurt a.M., Germany

    Rüdiger W. Brause

  2. Klinik für Allgemein- und Gefässchirurgie, Johann Wolfgang Goethe-Universität, Theodor-Stern-Kai 7, 60590, Frankfurt a.M., Germany

    Ernst Hanisch

Rights and permissions

Reprints and permissions

Copyright information

© 2000 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Maojo, V., Sanandrés, J. (2000). A Survey of Data Mining Techniques. In: Brause, R.W., Hanisch, E. (eds) Medical Data Analysis. ISMDA 2000. Lecture Notes in Computer Science, vol 1933. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39949-6_4

Download citation

  • DOI: https://doi.org/10.1007/3-540-39949-6_4

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41089-8

  • Online ISBN: 978-3-540-39949-0

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation