Skip to main content
SpringerLink
Log in

Bayesian Network Mining System

  • Conference paper
Intelligent Information Systems 2001

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 10))

  • 119 Accesses

Abstract

The paper provides an exhaustive description of a new system serving learning, viewing and reasoning with Bayesian networks.

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 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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.

References

  1. S. Acid, L.M. de Campos: An algorithm for finding minimum dseparating sets in belief networks, forthcoming, 1996.

    Google Scholar 

  2. Chow C.K., Liu C.N. Approximating discrete probability distributions with depndence trees. IEEE Trans. on Information Theory 14(3), 1968, 462–467.

    Article  MathSciNet  MATH  Google Scholar 

  3. G.F.Cooper, E. Herskovits: A Bayesian method for the induction of probabilistic networks from data. Machine Learning 9 (1992), 309–347

    MATH  Google Scholar 

  4. D.Geiger, T.Verma, J. Pearl: d–Separation: From theorems to algorithms. In: M.Henrion, R.D.Shachter, L.N.Kamal, J.F.Lemmer (eds): Uncertainty in Artificial Intelligence 5, Elsevier Science Publishers B.V. (North–Holland), 1990, 139–148

    Google Scholar 

  5. M.A. Klopotek: Partial Dependency Separation – a new concept for expressing dependence/independence relations in causal networks. Demonstratio Mathematica. Vol XXXII No 1,1999, pp. 207–226.

    MathSciNet  Google Scholar 

  6. M.A.Klopotek: Methods of Identification and Interpretations of Belief Distributions in the Dempster2013Shafer Theory (in Polish), IPI PAN Publisher, Warsaw 1998, ISBN 83–900820–8–x.

    Google Scholar 

  7. M.A. Klopotek, S.T. Wierzchon: Partial D–Separation for Discovery of Bayesian Networks from Data. R. Trappl, ed.:Cybernetics and Systems. Proc. EMCSR'2000, 25–28.4.2000, Vienna, Austrian Society for Cybernetics, Vol. 2, pp. 707–712.

    Google Scholar 

  8. W. Lam and F. Bacchus: Learning Bayesian Belief Networks: An Approach based on the MDL Principle, Computational Intelligence, vol. 10, pp. 269–293, 1994.

    Article  Google Scholar 

  9. J. Pearl: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann, San Mateo CA, 1988

    Google Scholar 

  10. G. Shafer, P.P. Shenoy, R. Logan: Propagating belief functions in qualitative Markov Trees. Intn. J. of Approximate Reasoning,1(1987)349–400

    Article  MATH  Google Scholar 

  11. P. Spirtes, C. Glymour, R. Scheines: Causality from probability. In: G. McKee ed.: Evolving knowledge in natural and artificial intelligence,London: Pitman, 1990, 181–199

    Google Scholar 

  12. P. Spirtes, C. Glymour, R. Scheines: Causation, Prediction and Search, Lecture Notes in Statistics 81, Springer–Verlag, 1993.

    Google Scholar 

  13. T. Urban, T. Kaempke: Recovering dependency graph in uncertain data. M. Mohammadian, ed: Computational Intelligence for Modelling, Control andAutomation. Evolutionary Computing and Fuzzy Logic for Intelligent Control, Knowledge Acquisition and Information Retrieval. Concurrent Systems Engineering Series Vol. 55. IOS Press Amsterdam, Berlin, Oxford 1999, pp. 476–481.

    Google Scholar 

  14. S.T. Wierzchon,Knowledge Representation and Manipulation within DempsterShafer Framework (in Polish), IPI PAN Publisher, Warsaw 1996, ISBN 83–900820–520135, 215 pp.

    Google Scholar 

  15. Y. Xiang, S.K.M. Wong and N. Cercone: A`Microscopic' study of minimum entropy search in learning decomposable Markov networks. Machine Learning, Vol. 26, No. 1, 6520 1392, 1997.

    Google Scholar 

  16. Proposal of a Bayesian Network InterOperab Address="http://www.ipipan.waw.pl"crit2/index.html

    Google Scholar 

  17. dBASE III,IV,5 DBF file format:http://www.apptools.com/dbase/faq/qformt.htm

  18. JavaBayes. URL:http://www.cs.cmu.edu/People/javabayes/index.html, its Bayesian network formats BIF version 0.15BIF version 0.1 XMLBIF ver. 0.3 http://www.cs.cmu.edu/javabayes/Home/node7.html#SECTION0071000000 0000000000

  19. Microsoft Belief Network Tools for Bayesian Inference, URL: http://www.research.microsoft.com/research/dtg/msbn, its XML Belief Network File Format:http://www.research.microsoft.com/dtas/bnformat

  20. MCL++ Library of Machine Learning functions:http://www.sgi.com/Technology/m1c

  21. Information about Hugin: URL:http://www.hugin.dk

Download references

Author information

Authors and Affiliations

  1. Institute of Computer Science, ,Polish Academy of Sciences, Warsaw, Poland

    Mieczysław A. Kłopotek, Sławomir T. Wierzchoń, Maciej Michalewicz, Marek Bednarczyk & Wiesław Pawłowski

  2. Faculty of Mathematics and Information Science, Warsaw University of Technology, USA

    Mieczysław A. Kłopotek & Andrzej Wąsowski

Authors
  1. Mieczysław A. Kłopotek
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sławomir T. Wierzchoń
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maciej Michalewicz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Marek Bednarczyk
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wiesław Pawłowski
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Andrzej Wąsowski
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Polish Academy of Sciences, ul. Ordona 21, 01-237, Warsaw, Poland

    Mieczysław A. Kłopotek , Maciej Michalewicz  & Sławomir T. Wierzchoń ,  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kłopotek, M.A., Wierzchoń, S.T., Michalewicz, M., Bednarczyk, M., Pawłowski, W., Wąsowski, A. (2001). Bayesian Network Mining System. In: Kłopotek, M.A., Michalewicz, M., Wierzchoń, S.T. (eds) Intelligent Information Systems 2001. Advances in Intelligent and Soft Computing, vol 10. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1813-0_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-7908-1813-0_16

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-1407-1

  • Online ISBN: 978-3-7908-1813-0

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation