Advertisement

A Framework for Mining Association Rules in Data Warehouses

  • Haorianto Cokrowijoyo Tjioe
  • David Taniar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3177)

Abstract

The effort of data mining, especially in relation to association rules in real world business applications, is significantly important. Recently, association rules algorithms have been developed to cope with multidimensional data. In this paper we are concerned with mining association rules in data warehouses by focusing on its measurement of summarized data. We propose two algorithms: HAvg and VAvg, to provide the initialization data for mining association rules in data warehouses by concentrating on the measurement of aggregate data. These algorithms are capable of providing efficient initialized data extraction from data warehouses and are used for mining association rules in data warehouses.

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: SIGMOD 1993, pp. 207–216 (1993)Google Scholar
  2. 2.
    Bodon, F.: A Fast Apriori Implementation. In: FIMI 2003 (November 2003)Google Scholar
  3. 3.
    Chaudhuri, S., Dayal, U.: An Overview of Data Warehousing and OLAP Technology. ACM SIGMOD Record 26, 65–74 (1997)CrossRefGoogle Scholar
  4. 4.
    Guenzel, H., Albrecht, J., Lehner, W.: Data Mining in a Multidimensional Environment. In: Eder, J., Rozman, I., Welzer, T. (eds.) ADBIS 1999. LNCS, vol. 1691, pp. 191–204. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  5. 5.
    Kamber, M., Han, J., Chiang, J.Y.: Metarule-Guided Mining of Multi-Dimensional Association Rules Using Data Cubes. In: KDD 1997, August 1997, pp. 207–210 (1997)Google Scholar
  6. 6.
    Oracle, Oracle 9i Data Warehouse Guide (2001), http://www.oracle.com
  7. 7.
    Park, J.S., Chen, M.S., Yu, P.S.: An Effective Hash based Algorithm for Mining Association Rules. In: SIGMOD 1995, May 1995, pp. 175–186 (1995)Google Scholar
  8. 8.
    Savasere, A., Omiecinski, E., Navathe, S.: An Efficient Algorithm for Mining Association Rules in Large Databases. In: VLDB 1995, pp. 432–444 (1995)Google Scholar
  9. 9.
    Srikant, R., Agrawal, R.: Mining Generalized Association Rules. In: VLDB 1995, pp. 407–419 (1995)Google Scholar
  10. 10.
    Srikant, R., Agrawal, R.: Mining Quantitative Association Rules in Large Relational Tables. In: SIGMOD 1996, pp. 1–12 (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Haorianto Cokrowijoyo Tjioe
    • 1
  • David Taniar
    • 1
  1. 1.School of Business SystemsMonash UniversityClaytonAustralia

Personalised recommendations