Advertisement

Performance Evaluation of SQL-OR Variants for Association Rule Mining

  • P. Mishra
  • S. Chakravarthy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2737)

Abstract

In this paper, we focus on the SQL-OR approaches. We study several additional optimizations for the SQL-OR approaches (Vertical Tid, Gather-join, and Gather count) and evaluate them using DB2 and Oracle RDBMSs. We evaluate the approaches analytically and compare their performance on large data sets. Finally, we summarize the results and indicate the conditions for which the individual optimizations are useful.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules between sets of items in large databases. In: ACM SIGMOD (1993)Google Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Fast Algorithms for mining association rules. In: 20th Int’l Conference on Very Large Databases, VLDB (1994)Google Scholar
  3. 3.
    Savasere, A., Omiecinsky, E., Navathe, S.: An efficient algorithm for mining association rules in large databases. In: 21st Int’l Cong. on Very Large Databases, VLDB (1995)Google Scholar
  4. 4.
    Shenoy, P., et al.: Turbo-charging Vertical Mining of Large Databases. In: SIGMOD (2000)Google Scholar
  5. 5.
    Han, J., Pei, J., Yin, Y.: Mining Frequent Patterns wihtout Candidate Generation. In: ACM SIGMOD (2000)Google Scholar
  6. 6.
    Houtsma, M., Swami, A.: Set-Oriented Mining for Association Rules in Relational Databases. In: ICDE (1995)Google Scholar
  7. 7.
    Han, J., et al.: DMQL: A data mining query language for relational database. In: ACM SIGMOD workshop on research issues on data mining and knowledge discovery (1996)Google Scholar
  8. 8.
    Meo, R., Psaila, G., Ceri, S.: A New SQL-like Operator for Mining Association Rules. In: Proc. of the 22nd VLDB Conference, India (1996)Google Scholar
  9. 9.
    Agrawal, R., Shim, K.: Developing tightly-coupled Data Mining Applications on a Relational Database System, IBM Report (1995)Google Scholar
  10. 10.
    Sarawagi, S., Thomas, S., Agrawal, R.: Integrating Association Rule Mining with Rekational Database System: Alternatives and Implications. In: ACM SIGMOD 1998 (1998)Google Scholar
  11. 11.
    Thomas, S.: Architectures and optimizations for integrating Data Mining algorithms with Database Systems. In: CSE. University of Florida, Gainesville (1998)Google Scholar
  12. 12.
    Dudgikar, M.: A Layered Optimizer or Mining Association Rules over RDBMS. In: CSE Department. University of Florida, Gainesville (2000)Google Scholar
  13. 13.
    Mishra, P.: Evaluation of K-way Join and its variants for Association Rule Mining. MS Thesis, Information and Technology Lab and CSE Department at UT Arlington, TX (2002)Google Scholar
  14. 14.
    Mishra, P., Chakravarthy, S.: Performance Evaluation and Analysis of SQL-92 Approaches for Association Rule Mining. In: James, A., Younas, M., Lings, B. (eds.) BNCOD 2003. LNCS, vol. 2712. Springer, Heidelberg (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • P. Mishra
    • 1
  • S. Chakravarthy
    • 1
  1. 1.Information and Technology Laboratory and CSE DepartmentThe University of Texas at ArlingtonArlingtonUSA

Personalised recommendations