Skip to main content

Relational Association Rules: Getting Warmer

  • Conference paper
  • First Online:
Pattern Detection and Discovery

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2447))

Abstract

In recent years, the problem of association rule mining in transactional data has been well studied. We propose to extend the discovery of classical association rules to the discovery of association rules of conjunctive queries in arbitrary relational data, inspired by the Warmr algorithm, developed by Dehaspe and Toivonen, that discovers association rules over a limited set of conjunctive queries. Conjunctive query evaluation in relational databases is well understood, but still poses some great challenges when approached from a discovery viewpoint in which patterns are generated and evaluated with respect to some well defined search space and pruning operators.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A.I. Verkamo. Fast discovery of association rules. In U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, eds., Advances in Knowledge Discovery and Data Mining, pages 307–328. MIT Press, 1996.

    Google Scholar 

  2. J-F. Boulicaut, A. Bykowski, and C. Rigotti. Free-sets: a condensed representation of boolean data for frequency query approximation. Data Mining and Knowledge Discovery, 2001. To appear.

    Google Scholar 

  3. T. Calders and B. Goethals. Mining all non-derivable frequent itemsets. In Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery, Lecture Notes in Computer Science. Springer-Verlag, 2002. to appear.

    Google Scholar 

  4. C. Chekuri and A. Rajaraman. Conjunctive query containment revisited. Theoretical Computer Science, 239(2):211–229, 2000.

    Article  MATH  MathSciNet  Google Scholar 

  5. L. Dehaspe and H. Toivonen. Discovery of frequent datalog patterns. Data Mining and Knowledge Discovery, 3(1):7–36, 1999.

    Article  Google Scholar 

  6. L. Dehaspe and H. Toivonen. Discovery of relational association rules. In S. Dzeroski and N. Lavrac, eds., Relational data mining, pages 189–212. Springer-Verlag, 2001.

    Google Scholar 

  7. S. Fortin. The graph isomorphism problem. Technical Report 96-20, University of Alberta, Edmonton, Alberta, Canada, July 1996.

    Google Scholar 

  8. H. Garcia-Molina, J. Ullman, and J. Widom. database system implementation. Prentice-Hall, 2000.

    Google Scholar 

  9. D. Hand, H. Mannila, and P. Smyth. Principles of Data Mining. MIT Press, 2001.

    Google Scholar 

  10. A. Inokuchi and H. Motoda T. Washio. An apriori-based algorithm for mining frequent substructures from graph data. In Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery, volume 1910 of Lecture Notes in Computer Science, pages 13–23. Springer-Verlag, 2000.

    Chapter  Google Scholar 

  11. M. Kuramochi and G. Karypis. Frequent subgraph discovery. In Proceedings of the 2001 IEEE International Conference on Data Mining, pages 313–320. IEEE Computer Society, 2001.

    Google Scholar 

  12. H. Mannila and H. Toivonen. Levelwise search and borders of theories in knowledge discovery. Data Mining and Knowledge Discovery, 1(3):241–258, November 1997.

    Article  Google Scholar 

  13. S.H. Nienhuys-Cheng and R. de Wolf. Foundations of Inductive Logic Programming, volume 1228 of Lecture Notes in Artificial Intelligence. Springer-Verlag, 1997.

    Google Scholar 

  14. N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal. Discovering frequent closed itemsets for association rules. In Proceedings of the 7th International Conference on Database Theory, volume 1540 of Lecture Notes in Computer Science, pages 398–416. Springer-Verlag, 1999.

    Google Scholar 

  15. P. Roy, S. Seshadri, S. Sudarshan, and S. Bhobe. Efficient and extensible algorithms for multi query optimization. In Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, volume 29:2 of SIGMOD Record, pages 249–260. ACM Press, 2000.

    Article  Google Scholar 

  16. J.D. Ullman. Principles of database and knowledge-base systems, volume 2, volume 14 of Principles of Computer Science. Computer Science Press, 1989.

    Google Scholar 

  17. M. Zaki. Efficiently mining frequent trees in a forest. In Proceedings of the Eight ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press, 2002. to appear.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Goethals, B., Van den Bussche, J. (2002). Relational Association Rules: Getting Warmer . In: Hand, D.J., Adams, N.M., Bolton, R.J. (eds) Pattern Detection and Discovery. Lecture Notes in Computer Science(), vol 2447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45728-3_10

Download citation

  • DOI: https://doi.org/10.1007/3-540-45728-3_10

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44148-9

  • Online ISBN: 978-3-540-45728-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics