Parallel knowledge discovery using domain generalization graphs

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1263, Issue , 1997, Pages 25-35

  Hilderman, Robert J ^a   Hamilton, Howard J ^a   Kowalchuk, Robert J ^a   Cercone, Nick ^a  

^a UNIVERSITY OF REGINA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

GRAPH THEORY; GRAPHIC METHODS;

ATTRIBUTE ORIENTED INDUCTION; INHERENT PARALLELISM; MULTI-ATTRIBUTES; MULTIPLE PROCESSORS; PARALLEL IMPLEMENTATIONS; RELATIONAL DATABASE;

DATA MINING;

EID: 84947706422     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-63223-9_103     Document Type: Conference Paper

References (11)

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3. C. L. Carter and H. J. Hamilton. A fast, on-line generalization algorithm for knowledge discovery. Applied Mathematics Letters, 8(2):S-11, 1995.
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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.
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.
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.