Knowledge discovery from databases with the guidance of a causal network

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

Volumn 1325, Issue , 1997, Pages 401-410

  Zhu, Qiuming ^a   Chen, Zhengxin ^a  

^a UNIVERSITY OF NEBRASKA AT OMAHA   (United States)

Author keywords

Causal networks; Goal driven; Knowledge discovery from databases

Indexed keywords

DATABASE SYSTEMS; INTELLIGENT SYSTEMS;

CAUSAL NETWORK; FUNDAMENTAL FEATURES; GOAL DRIVEN; INTERACTIVE DATA MINING; KNOWLEDGE DISCOVERY FROM DATABASE; KNOWLEDGE DISCOVERY PROCESS; PROBLEM DOMAIN; USER INTENTION;

DATA MINING;

EID: 84947750288     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-63614-5_39     Document Type: Conference Paper

References (11)

1. R. L. Blum, "Induction of causal relationships from a time-oriented clinical database: An overview of RX project," Proceedings of Second National Conference on Artificial Intelligence, MIT Press, Cambridge, MA, pp. 355-357, 1982.
2. N. Cercone, and M. Tsuchiya (Guest eds.), 1993, Special issue on Learning and Discovery in knowledge-based databases, IEEE Trans. Knowl. Data Eng., Vol. 5, No. 6, 1993.
3. G. F. Cooper and E. Herskovits, "A Bayesian method for the induction of probabilistic networks from data," Machine Learning, Vol. 9, No. 4, pp. 309-348, 1994.
4. R. G. Cowell, A. P. Dawid, and D. J. Spiegelhalter, "Sequential model criticism in probabilistic expert systems," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 15, No. 3, pp. 209-219, 1993.
5. J. Han, Y. Cai and N. Cercone, Data-driven discovery of quantitative rules in relational databases, IEEE Trans. Knowledge and Data Engineering, Vol. 5, No. 1, pp. 29-40, 1993.
6. E. Herskovits and G. F. Cooper, "Kutato: An entropy-driven system for construction of probabilistic expert systems from databases," Uncertainty in Artificial Intelligence, Amsterdam, North Holland, pp. 117-125, 1991.
7. J. Pearl, Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, Palo Alto, CA, Second printing, 1991.
8. G. Piatetsky-Shapiro, and W. J. Frawley (eds.), Knowledge Discovery in Databases, Menlo Park, CA: ALBRIGHT UNIV. AI/MIT Press, 1991.
9. G. Piatetsky-Shapiro, et, al, "KDD-93: Progress and challenges in knowledge discovery in databases," Al Magazine, Vol. 15, No. 3, pp. 77-82, 1994.
10. M. Provan and J. R. Clarke, "Dynamic network construction and updating techniques for the diagnosis of Acute Abdominal Pain," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 15, No. 3, pp. 299-307, 1993.
11. S. K. M. Wong and P. Lingras, "Representation of qualitative user preference by quantitative belief functions," IEEE Transactions on Knowledge and Data Engineering, Vol. 6, No. 1, pp. 72-78, 1993.