Advertisement

On the Equivalence of Top-down and Bottom-up Data Mining in Relational Databases

  • Hasan M. Jamil
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2114)

Abstract

Although knowledge discovery from large relational databases has gained popularity, and its significance is well recognized, the prohibitive nature of the cost associated with extracting such knowledge, and the lack of suitable declarative query language support, still act as limiting factors. Surprisingly, little or no relational technology has not yet been significantly exploited in data mining even though data often reside in relational tables. Consequently, no relational optimization has yet been possible for data mining. We exploit the transitive nature of large item sets and the so called anti-monotonicity property of support thresholds of large item sets to develop a natural least fixpoint operator for data mining. The operator proposed has several advantages including optimization opportunities, and traditional candidate set free large item set generation. We present an AQL3 expression for association rule mining and discuss its mapping to the least fixpoint operator developed in this paper, and thereby establish the equivalence of the top-down and bottom-up computation of large item sets in relational databases.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Rakesh Agrawal, Tomasz Imielinski, and Arun N. Swani. Mining association rules between sets of items in large databases. In Peter Buneman and Sushil Jajodia, editors, Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pages 207–216, Washington, D.C., May 26–28 1993.Google Scholar
  2. [2]
    Rakesh Agrawal, and Ramakrishman Srikant. Fast algorithms for mining association rules in large databases. In orge B. Bocca, Matthias Jarke, and Carlo Zaniolo, editors, Proceedings of 20th International Conference on Very Large Data Bases, pages 487–499, Santiago, Chile, September 12–15 1994.Google Scholar
  3. [3]
    Yves Bastide, Rafik Taouil, Nicolas Pasquier, Gerd Stumme, and LOtfi Lakhal. Mining frequent patterns with counting inference. SIGKDD Explorations, 2(2):66–75, 2000.CrossRefGoogle Scholar
  4. [4]
    Hasan M. Jamil. Surprise! SQL is enough for data mining. Technical report, November 2000.Google Scholar
  5. [5]
    Rosa Meo, Guiseppe Psaila, and Stefano Ceri. A new SQL-like operator for mining association rules. In T. M. Vijayaraman, Alejandro P. Buchmann, C. Mohan, and Nandlal L. Sarda, editors, Proceedings of 22nd International Conference on Very Large Data Bases, pages 122–133, Mumbai, India, September 3–6 1996.Google Scholar
  6. [6]
    Rosa Meo, Guiseppe Psaila, and Stefano Ceri. An extension to SQL for mining association rules. Data Mining and Knowledge Discovery, 2(2):195–224, 1998CrossRefGoogle Scholar
  7. [7]
    Karthick Rajamani, Alan Cox, Bala Iyer, and Atul Chadha. Efficient mining for association rules with relational database systems. In Proceedings of the International Database Engineering and Applications Symposium, pages 148–155, 1999Google Scholar
  8. [8]
    Sunita Sarawagi, Shiby Thomas, and Rakesh Agrawal. Integrating mining with relational database systems: Alternative and implications. In Laura M. Haas and Ashutosh Tiwary, editors, Proceedings of the ACM SIGMOD International Conference on MAnagement of Data, pages 343–354, Seattle, Washington, June 2–4 1998.Google Scholar
  9. [9]
    Shiby Thomas and Sunita Sarawagi. Mining generalized association rules and sequential patterns using SQL queries. In Proceedings of the sixth ACM SIGKDD international conference on knowledge discovery and data mining, pages 344–348, New York, NY, Auguast 1998.Google Scholar
  10. [10]
    Mohammed Javeed Zaki. Generating non-redundant association rules. In Proceedings of the sixth ACM SIGKDD international conference on knowledge discovery and data mining, pages 34–43, Boston, MA USA, August 2000. ACM Press.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Hasan M. Jamil

There are no affiliations available

Personalised recommendations