Efficient Search for Strong Partial Determinations

Proceedings - 2nd International Conference on Knowledge Discovery and Data Mining, KDD 1996

Volumn , Issue , 1996, Pages 371-374

  Kramer, Stefan ^a   Pfahringer, Bernhard ^a  

^a AUSTRIAN RESEARCH INSTITUTE FOR ARTIFICIAL INTELLIGENCE   (Austria)

Author keywords

[No Author keywords available]

Indexed keywords

FUNCTIONAL DEPENDENCY; OVERFITTING; REAL-WORLD; RUNTIMES;

EID: 0344109301     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (11)

1. Agrawal R. and Srikant R.: Past Algorithms for Mining Association Rules. Proceedings of the 20"1 VLDB Conference, Santiago, Chile, 1994.
2. Flach P.A.: Predicate Invention in Inductive Data Engineering, in Brazdil P.B.(ed.), Machine Learning: ECML-93, Springer, Berlin, pp.83-94, 1993.
3. Kivinen J., Mannila H.: Approximate dependency inference from relations, Theoretical Computer Science, 149(1):129-149, 1995.
4. Mannila H. and Raiha K.-J.: Algorithms for Inferring Functional Dependencies From Relations. Data & Knowledge Engineering 12 (1994) 83-99, 1994.
5. Pfahringer B., Kramer S.: Compression-Based Evaluation of Partial Determinations, Proceedings of the First International Conference on Knowledge Discovery and Data Mining, AAAI Press, 1995.
6. J. Rissanen: Modeling by Shortest Data Description. In: Automatica, 14:465-471, 1978.
7. Russell S.J.: The Use of Knowledge in Analogy and Induction. Pitman Publishing, London, 1989.
8. Schlimmer J.C.: Efficiently Inducing Determinations: A Complete and Systematic Search Algorithm that Uses Optimal Pruning. Proceedings of the lOth International Conference on Machine Learning, 1993.
9. Shannon C.E. and Weaver W.: The Mathematical Theory of Communication, University of Illinois Press, 1949.
10. Shen W.-M.: Discovering Regularities from Large Knowledge Bases. Proceedings of the 8th International Workshop on Machine Learning, 1991.
11. Trappl R., Fiirnkranz J. and Petrak J.: Digging for Peace: Using Machine Learning Methods for Assessing International Conflict Databases. Proceedings of the 12"1 European Conference on Artificial Intelligence (ECAI-96), pp. 453-457, 1996.