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6. Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Proc.
of Thirteenth International Conference on Machine Learning, pp. 148-156 (1996)
7. Wolpert, D.: Stacked Generalization. Neural Network 5(2), 241-260 (1992)
8. Gama, J., Brazdil, P.: Cascade generalization. Machine Learning 41(3), 315-343
(2000)
9. Metal (2002), http://www.metal-kdd.org/
10. Bernstein, A., Provost, F.: An intelligent assistant for knowledge discovery process.
In: Proceeding IJICAI 2001 Workshop on Wrappers for Performance Enhancement
in KDD (2001)
11. Abe, H., Yamaguchi, T.: Constructive Meta-Learning with Machine Learning
Method Repositories. In: Orchard, B., Yang, C., Ali, M. (eds.) IEA/AIE 2004.
LNCS (LNAI), vol. 3029, pp. 502-511. Springer, Heidelberg (2004)
12. Hatazawa, H., Negishi, N., Suyama, A., Tsumoto, S., Yamaguchi, T.: Knowl-
edge Discovery Support from a Meningoencephalitis Database Using an Automatic
Composition Tool for Inductive Applications. In: Proc. of KDD Challenge, in con-
junction with PAKDD 2000, pp. 28-33 (2000)
13. Hettich, S., Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases,
Department of Information and Computer Science, University of California, Irvine
(1998), http://www.ics.uci.edu/mlearn/MLRepository.html
14. Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.): Explora:
A Multipattern and Multistrategy Discovery Assistant. In: Advances in Knowledge
Discovery and Data Mining, pp. 249-271. AAAI/MIT Press (1996)
15. Ali, K., Manganaris, S., Srikant, R.: Partial classification using association rules.
In: Proceedings of the International Conference on Knowledge Discovery and Data
Mining KDD 1997, pp. 115-118 (1997)
16. Brin, S., Motwani, R., Ullman, J., Tsur, S.: Dynamic itemset counting and im-
plication rules for market basket data. In: Proc. of ACM SIGMOD Int. Conf. on
Management of Data, pp. 255-264 (1997)
17. Rijsbergen, C.: Information Retrieval, Ch. 7, Butterworths, London (1979),
http://www.dcs.gla.ac.uk/Keith/Chapter.7/Ch.7.html
18. Gray, B., Orlowska, M.E.: CCAIIA: Clustering Categorical Attributes into Inter-
esting Association Rules. In: Pacific-Asia Conf. on Knowledge Discovery and Data
Mining PAKDD 1998, pp. 132-143 (1998)
19. Hamilton, H.J., Shan, N., Ziarko, W.: Machine Learning of Credible Classifications.
In: Proc. of Australian Conf. on Artificial Intelligence AI 1997, pp. 330-339 (1997)
20. Goodman, L.A., Kruskal, W.H.: Measures of association for cross classifications.
Springer Series in Statistics, vol. 1. Springer, Heidelberg (1979)
21. Smyth, P., Goodman, R.M.: Rule Induction using Information Theory. In:
Piatetsky-Shapiro, G., Frawley, W.J. (eds.) Knowledge Discovery in Databases,
pp. 159-176. AAAI/MIT Press (1991)
22. Piatetsky-Shapiro, G.: Discovery, Analysis and Presentation of Strong Rules. In:
Piatetsky-Shapiro, G., Frawley, W.J. (eds.) Knowledge Discovery in Databases,
pp. 229-248. AAAI/MIT Press (1991)
23. Gago, P., Bento, C.: A Metric for Selection of the Most Promising Rules. In:
European Conference on the Principles of Data Mining and Knowledge Discovery
PKDD 1998, pp. 19-27 (1998)
24. Zhong, N., Yao, Y.Y., Ohshima, M.: Peculiarity Oriented Multi-Database Mining.
IEEE Transaction on Knowledge and Data Engineering 15(4), 952-960 (2003)
25. Witten, I.H., Frank, E.: DataMining: Practical Machine Learning Tools and Tech-
niques with Java Implementations. Morgan Kaufmann, San Francisco (2000)
 
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