Rough Sets in Knowledge Discovery and Data Mining(<特集>ラフ集合の理論と応用)-Reference-Cited by-同舟云学术

Rough Sets in Knowledge Discovery and Data Mining(<特集>ラフ集合の理論と応用)

Published:2001 Issue:6 Volume:13 Page:581-591
ISSN:0915-647X
Container-title:Journal of Japan Society for Fuzzy Theory and Systems
language:en
Short-container-title:

Author:

ZHONG Ning¹

Affiliation:

1. Department of Information Engineering, Maebashi Institute of Technology

Publisher

Japan Society for Fuzzy Theory and Intelligent Informatics

Link

https://www.jstage.jst.go.jp/article/jfuzzy/13/6/13_KJ00002087336/_pdf

Reference47 articles.

1. 1) AGRAWAL R. Fast discovery of association rules. Advances in Knowledge Discovery and Data Mining. AAAI Press. (1996) p.307-328.

2. 2) BAZAN J. G. A Comparison of Dynamic and Non-dynamic Rough Set Methods for Extracting Laws from Decision System. Rough Sets in Knowledge Discovery 1 : Methodology and Applications. Physica-Verlag. (1998) p.321-365.

3. 3) CHMIELEWSKI M. R. Global Discretization of Attributes as Preprocessing for Machine Learning. Proc. Thrid Inter. Workshop on Rough Sets and Soft Computing. (1994) p.294-301.

4. 4) DOUGHERTY J. Supervised and Unsupervised Discretization of Real Features. Proc.12th Inter. Conf. on Machine Learning. (1995) p.194-202.

5. 5) FAYYAD U. M. On the Handling of Real-Valued Attributes in Decison Tree Generation. Machine Learning. (1992) vol.8, p.87-102.

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