Skip to main content
SpringerLink
Log in

Incomplete database issues for representative association rules

  • Communications
  • Conference paper
  • First Online:
Foundations of Intelligent Systems (ISMIS 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1609))

Included in the following conference series:

Abstract

Discovering association rules among items in large databases is recognized as an important database mining problem. It was originally introduced for sales transaction database. Usually the data mining research refers to complete databases. However, missing data often occur in relational databases, especially in business ones. In this paper, we investigate applicability of representative association rules in the context of incomplete databases. Relationship between the classes of representative and association rules satisfying conditions for optimistic, expected and pessimistic support and confidence is shown. In addition we discuss necessary modifications of the Apriori algorithm that enable mining for association rules in incomplete databases.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Agraval, R., Imielinski, T., Swami, A.: Mining Associations Rules between Sets of Items in Large Databases. In: Proc. of the ACM SIGMOD Conference on Management of Data. Washington, D.C. (1993) 207–216

    Google Scholar 

  2. Agraval, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo A.I.: Fast Discovery of Association Rules. In: Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.): Advances in Knowledge Discovery and Data Mining. AAAI, CA (1996) 307–328

    Google Scholar 

  3. Chmielewski, M.R., Grzymala-Busse, J.W., Peterson, N.W., Than S.: The Rule Induction System LERS—A Version for Personal Computers. In: Foundations of Computing and Decision Sciences, Vol. 18, No. 3-4 (1993) 181–212.

    Google Scholar 

  4. Deogun, J.S., Raghavan, V.V., Sarkar, A., Sever, H.: Data Mining: Trends in Research and Development. In: Lin, T.Y., Cercone, N. (eds.): Rough Sets and Data Mining. Kluwer Academic Publishers (1997) 9–45

    Google Scholar 

  5. Kononenko, I., Bratko, I., Roskar, E.: Experiments in Automatic Learning of Medical Diagnostic Rules. Technical Report. Jozef Stefan Institute, Ljubljana, Yugoslavia (1984)

    Google Scholar 

  6. Kryszkiewicz, M.: Representative Association Rules. In: Proc. of PAKDD ’98. Melbourne, Australia. LNAI 1394. Research and Development in Knowledge Discovery and Data Mining. Springer-Verlag (1998) 198–209

    Google Scholar 

  7. Kryszkiewicz, M.: Properties of Incomplete Information Systems in the Framework of Rough Sets: In Polkowski, L., Skowron, A. (eds.). Studies in Fuzziness and Soft Computing 18. Rough Sets in Knowledge Discovery 1. Physica-Verlag, Heidelberg (1998) 442–450

    Google Scholar 

  8. Kryszkiewicz, M., Rybinski H.: Incompleteness Aspects in the Rough Set Approach. In: Proc. of JCIS ’98. Raleigh, USA (1998) 371–374

    Google Scholar 

  9. Kryszkiewicz, M.: Association Rules in Incomplete Databases. In: Proc. of PAKDD-99. Beijing, China (1999)

    Google Scholar 

  10. Quinlan J.R.: Induction of Decision Trees. In: Shavlik J. W., Dietterich T. G. (eds.): Readings in Machine Learning. Morgan Kaufmann Publishers (1990) 57–69

    Google Scholar 

  11. Ragel A., Cremilleux B.: Treatment of Missing Values for Association Rules. In: Proc. of Second Pacific Asia Conference, PAKDD ’98. Melbourne, Australia. LNAI 1394. Research and Development in Knowledge Discovery and Data Mining. Springer (1998) 258–270

    Google Scholar 

  12. Slowinski R., Stefanowski J., Rough-Set Reasoning about Uncertain Data. In: Fundamenta Informaticae, Vol. 27, No. 2-3 (1996) 229–244

    Google Scholar 

  13. Srikant, R., Agraval, R.: Mining Generalized Association Rules. In: Proc. of the 21st VLDB Conference. Zurich, Swizerland (1995) 407–419

    Google Scholar 

  14. Walczak, Z., Selected Problems and Algorithms of Data Mining. M.Sc. Thesis (1998) Warsaw University of Technology (in Polish)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Computer Science, Warsaw University of Technology, Nowowiejska 15/19, 00-665, Warsaw, Poland

    Marzena Kryszkiewicz & Henryk Rybinski

Authors
  1. Marzena Kryszkiewicz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Henryk Rybinski
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Zbigniew W. Raś Andrzej Skowron

Rights and permissions

Reprints and permissions

Copyright information

© 1999 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kryszkiewicz, M., Rybinski, H. (1999). Incomplete database issues for representative association rules. In: Raś, Z.W., Skowron, A. (eds) Foundations of Intelligent Systems. ISMIS 1999. Lecture Notes in Computer Science, vol 1609. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0095147

Download citation

  • DOI: https://doi.org/10.1007/BFb0095147

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65965-5

  • Online ISBN: 978-3-540-48828-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation