Data-Driven Discovery of Quantitative Rules in Relational Databases

IEEE Transactions on Knowledge and Data Engineering

Volumn 5, Issue 1, 1993, Pages 29-40

  Han, Jiawei ^a   Cai, Yandong ^a   Cercone, Nick ^a  

^a SIMON FRASER UNIVERSITY   (Canada)

Author keywords

attribute oriented induction; characteristic rules; classification rules; data driven learning algorithms; Knowledge discovery in databases; machine learning; quantitative rules

Indexed keywords

LEARNING SYSTEMS; RULE BASED SYSTEMS;

ATTRIBUTE ORIENTED INDUCTION; INDUCTION METHODS; LARGE DATABASES; QUANTITATIVE RULES;

RELATIONAL DATABASE SYSTEMS;

EID: 0027542839     PISSN: 10414347     EISSN: None     Source Type: Journal    
DOI: 10.1109/69.204089     Document Type: Article

References (21)

1. Y. Cai, N. Cercone, and J. Han, “Attribute-oriented induction in relational databases,” in G. Piatetsky-Shapiro and W. J. Frawley, Eds. Knowledge Discovery in Databases. Menlo Park, CA: AAAI/MIT, 1990, pp. 213–228.
2. Y. Cai, N. Cercone, and J. Han, “An attribute-oriented approach for learning classification rules from relational databases,” in Proc. 6th Int. Conf. Data Engineering, Los Angeles, CA, Feb. 1990, 281–288.
3. U. S. Chakravarthy, J. Grant, and J. Minker, “Foundations of semantic query optimization for deductive databases,” in J. Minker, Ed. Foundations of Deductive Databases and Logic Programming, San Francisco. CA: Morgan Kaufmann, 1988, pp. 243–274.
4. K. C. C. Chan and A. K. C. Wong, “A statistical technique for extracting classificatory knowledge from databases,” in G. Piatetsky-Shapiro and W. J. Frawley, eds., Knowledge Discovery in Databases. Menlo Park, CA: AAAI/MIT, 1991, pp. 107–124.
5. P. Cohen and E. A. Feigenbaum, The Handbook of Artificial Intelligence (Vol. III). San Francisco, CA: William Kaufmann, 1983.
6. T. G. Dietterich and R. S. Michalski, “A comparative review of selected methods for learning from examples,” in R. S. Michalski et al., Eds., Machine Learning: An Artificial Intelligence Approach, Vol. 1. San Francisco, CA: Morgan Kaufmann, 1983, pp. 41–82.
7. D. Fisher, “Improving inference through conceptual clustering,” in Proc. 1987 AAAI Conf, Seattle, WA, July 1987, pp. 461-465.
8. H. Gallaire, J. Minker, and J. Nicolas, “Logic and databases: A deductive approach,” ACM Comput. Survey, vol. 16, no. 2, pp. 153–185, 1984.
9. M. Genesereth and N. Nilsson, Logical Foundations of Artificial Intelligence. San Francisco, CA: Morgan Kaufmann, 1987.
10. D. Haussler, “Quantifying the inductive bias in concept learning,” in Proc. 1986 AAAI Conf., Philadelphia, PA, Aug. 1986, pp. 485–489.
11. D. Kulkarni and H. A. Simon, “The process of scientific discovery: The strategy of experimentation,” Cognitive Sci., vol. 12, pp. 139-175, 1988.
12. M. V. Manago and Y. Kodratoff, “Noise and knowledge acquisition,” in Proc. 10th Int. Joint Conf. Artificial Intelligence, Milan, Italy, 1987, pp. 348–354.
13. R. S. Michalski, “A theory and methodology of inductive learning,” in R. S. Michalski et al., Eds., Machine Learning: An Artificial Intelligence Approach, Vol. 1. San Francisco, CA: Morgan Kaufmann, 1983, pp. 83–134.
14. R. S. Michalski, J. G. Carbonell, and T. M. Mitchell, Machine Learning, An Artificial Intelligence Approach, Vol. 2. San Francisco, CA: Morgan Kaufmann, 1986.
15. T. M. Mitchell, “Version spaces: A candidate elimination approach to rule learning,” Proc. 5th Int. Joint Conf. Artificial Intelligence, Cambridge, MA, 1977, pp. 305–310.
16. G. Piatetsky-Shapiro, “Discovery of strong rules in databases,” in G. Piatetsky-Shapiro and W. J. Frawley, eds., Knowledge Discovery in Databases. Menlo Park, CA: AAAI/MIT, 1991, pp. 229–238.
17. J. R. Quinlan, “The effect of noise on concept learning,” in R. S. Michalski et al, Eds., Machine Learning: An Artificial Intelligence Approach, Vol. 2. San Francisco, CA: Morgan Kaufmann, 1986, pp. 149–166.
18. S. J. Russell, “Tree-structured bias,” in Proc. 1988 AAAI Conf, Minneapolis, MN, Aug. 1988, pp. 641–645.
19. M. Stonebraker, Readings in Database Systems. San Francisco, CA: Morgan Kaufmann, 1988.
20. D. Subramanian and J. Feigenbaum, “Factorization in experiment generation,” in Proc. 1986 AAAI Conf, Philadelphia, PA, Aug. 1986, pp. 518–522.
21. J. D. Ullman, Principles of Database and Knowledge-Base Systems, Vol. 1. Palo Alto, CA: Computer Science, 1988.