Advertisement

Parallel knowledge discovery using domain generalization graphs

  • Robert J. Hilderman
  • Howard J. Hamilton
  • Robert J. Kowalchuk
  • Nick Cercone
Plenary Session 1
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1263)

Abstract

Multi-Attribute Generalization is an algorithm for attribute-oriented induction in relational databases using domain generalization graphs. Each node in a domain generalization graph represents a different way of summarizing the domain values associated with an attribute. When generalizing a set of attributes, we show how a serial implementation of the algorithm generates all possible combinations of nodes from the domain generalization graphs associated with the attributes, resulting in the presentation of all possible generalized relations for the set. We then show how the inherent parallelism in domain generalization graphs is exploited by a parallel implementation of the algorithm. Significant speedups were obtained using our approach when large discovery tasks were partitioned across multiple processors. The results of our work enable a database analyst to quickly and efficiently analyze the contents of a relational database from many different perspectives.

References

  1. 1.
    C. L. Carter and H. J. Hamilton. Efficient attribute-oriented algorithms for knowledge discovery from large databases. To appear in IEEE Trans. on Knowledge and Data Engineering.Google Scholar
  2. 2.
    C. L. Carter and H. J. Hamilton. Fast, incremental generalization and regeneralization for knowledge discovery from databases. In Proceedings of the 8th Florida Artificial Intelligence Symposium, pages 319–323, Melbourne, Florida, April 1995.Google Scholar
  3. 3.
    C. L. Carter and H. J. Hamilton. A fast, on-line generalization algorithm for knowledge discovery. Applied Mathematics Letters, 8(2):5–11, 1995.CrossRefGoogle Scholar
  4. 4.
    C. L. Carter and H. J. Hamilton. Performance evaluation of attribute-oriented algorithms for knowledge discovery from databases. In Proceedings of the Seventh IEEE International Conference on Tools with Artificial Intelligence (ICTAI'95), pages 486–489, Washington, D.C., November 1995.Google Scholar
  5. 5.
    H. J. Hamilton, R. J. Hilderman, and N. Cercone. Attribute-oriented induction using domain generalization graphs. In Proceedings of the Eighth IEEE International Conference on Tools with Artificial Intelligence (ICTAI'96), pages 246–253, Toulouse, France, November 1996.Google Scholar
  6. 6.
    H.J. Hamilton and D.F. Fudger. Measuring the potential for knowledge discovery in databases with DBLearn. Computational Intelligence, 11(2):280–296, 1995.Google Scholar
  7. 7.
    J. Han. Towards efficient induction mechanisms in database systems. Theoretical Computer Science, 133:361–385, October 1994.CrossRefGoogle Scholar
  8. 8.
    J. Han, Y. Cai, and N. Cercone. Knowledge discovery in databases: an attribute-oriented approach. In Proceedings of the 18th International Conference on Very Large Data Bases, pages 547–559, Vancouver, August 1992.Google Scholar
  9. 9.
    J. Han, Y. Cai, and N. Cercone. Data-driven discovery of quantitative rules in relational databases. IEEE Trans. on Knowledge and Data Engineering, 5(1):29–40, February 1993.CrossRefGoogle Scholar
  10. 10.
    H.-Y. Hwang and W.-C. Fu. Efficient algorithms for attribute-oriented induction. In Proceedings of the 1st International Conference on Knowledge Discovery and Data Mining (KDD-95), pages 168–173, Montreal, August 1995.Google Scholar
  11. 11.
    W. Pang, R.J. Hilderman, H.J. Hamilton, and S.D. Goodwin. Data mining with concept generalization graphs. In Proceedings of the Ninth Annual Florida AI Research Symposium, pages 390–394, Key West, Florida, May 1996.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Robert J. Hilderman
    • 1
  • Howard J. Hamilton
    • 1
  • Robert J. Kowalchuk
    • 1
  • Nick Cercone
    • 1
  1. 1.Department of Computer ScienceUniversity of ReginaReginaCanada

Personalised recommendations