Advertisement

Journal of Intelligent Information Systems

, Volume 12, Issue 1, pp 61–73 | Cite as

Borders: An Efficient Algorithm for Association Generation in Dynamic Databases

  • Yonatan Aumann
  • Ronen Feldman
  • Orly Lipshtat
  • Heikki Manilla
Article

Abstract

We consider the problem of finding association rules in a database with binary attributes. Most algorithms for finding such rules assume that all the data is available at the start of the data mining session. In practice, the data in the database may change over time, with records being added and deleted. At any given time, the rules for the current set of data are of interest. The naive, and highly inefficient, solution would be to rerun the association generation algorithm from scratch following the arrival of each new batch of data. This paper describes the Borders algorithm, which provides an efficient method for generating associations incrementally, from dynamically changing databases. Experimental results show an improved performance of the new algorithm when compared with previous solutions to the problem.

association rules knowledge discovery data mining 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Agrawal, A., Imielinski, T., and Swami, A. (1993). Mining association rules between sets of items in large databases. In Proc. of the ACM SIGMOD Conference on Management of Data (pp. 207–216).Google Scholar
  2. Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., and Verkamo, I. (1995). Fast Discovery of Association Rules. In U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy (Eds.), Advances in Knowledge Discovery and Data Mining (pp. 307–328). AAAI Press.Google Scholar
  3. Agrawal, A. and Srikant, R. (1994). Fast algorithms for mining association rules. In Proceedings of the VLDB Conference. Santiago, Chile.Google Scholar
  4. Agrawal, R. and Srikant, R. (1995). Mining sequential patterns. Proc. of the Int'l Conference on Data Engineering (ICDE). Taipei, Taiwan.Google Scholar
  5. Cheung, D.W., Han, J., Ng, V., and Wong, C.Y. (1996). Maintenance of discovered association rules in large databases: An incremental updating techniques. Proc. 12th IEEE International Conference on Data Engineering (ICDE-96). New Orleans, Louisiana, U.S.A.Google Scholar
  6. Cheung, D.W., Lee, S.D., and Kao, B. (1997). A general incremental technique for updating discovered association rules. Proc. International Conference On Database Systems for Advanced Applications (DASFAA-97). Melbourne, Australia.Google Scholar
  7. Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., and Uthurusamy, R. (1995). Advances in Knowledge Discovery and Data Mining, AAAI Press.Google Scholar
  8. Feldman, R., Amir, A., Aumann, Y., Zilberstein, A., and Hirsh, H. (1997). Incremental algorithms for association generation. In Proceedings of the 1st Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD97). Singapore.Google Scholar
  9. Feldman, R., Fresko, M., Kinar, Y., Liphstat, O., Schler, Y., Rajman, M., and Zamir, O. (1998). Text Mining at the Term Level. Department of computer science technical report. Bar Ilan University.Google Scholar
  10. Klemettinen, M., Mannila, H., Ronkainen, P., Toivonen, H., and Verkamo, A. (1994). Finding interesting rules from large sets of discovered association rules. In Proceedings of the 3rd International Conference on Information and Knowledge Management.Google Scholar
  11. Mannila, H. and Toivonen, H. (1997). Levelwise Search and Borders of Theories in Knowledge Discovery, Data Mining and Knowledge Discovery, 1(3), 241–258.Google Scholar
  12. Mannila, H., Toivonen, H., and Verkamo, A. (1994). Efficient algorithms for discovering association rules. In Proceedings of KDD94: AAAI Workshop on Knowledge Discovery in Databases (pp. 181–192).Google Scholar
  13. Savasere, A., Omiecinski, E., Navathe, S. (1995). An efficient algorithm for mining association rules in large databases. In Proceedings of the 21st VLDB Conference. Zurich, Switzerland.Google Scholar
  14. Srikant, R. and Agrawal, R. (1996). Mining quantitative association rules in large relational tables. In Proceedings of the ACM SIGMOD Conference on Management of Data. Montreal, Canada.Google Scholar

Copyright information

© Kluwer Academic Publishers 1999

Authors and Affiliations

  • Yonatan Aumann
    • 1
  • Ronen Feldman
    • 1
  • Orly Lipshtat
    • 1
  • Heikki Manilla
    • 1
  1. 1.Department of Mathematics and Computer ScienceBar-Ilan UniversityRamat-GanIsrael

Personalised recommendations