Fundamentals of Rough Set Based Data Analysis
Author:
Affiliation:
1. Graduate School of Humanities and Sustainable System Sciences, Osaka Prefecture University
Publisher
Japan Society for Fuzzy Theory and Intelligent Informatics
Link
https://www.jstage.jst.go.jp/article/jsoft/32/4/32_128/_pdf
Reference23 articles.
1. [1] Z. Pawlak: “Rough sets,” Int. J. of Computer and Information Sciences, Vol.11, No.5, pp. 341-356, 1982.
2. [2] J. Kacprzyk and W. Pedrycz eds.: “Part C: Rough Sets,” in Springer Handbook of Computational Intelligence, Springer, 2015.
3. [3] L. S. Riza, A. Janusz, C. Bergmeir, C. Cornelis, F. Herrera, D. Ślęzak, and J. M. Benítez: “Implementing algorithms of rough set theory and fuzzy rough set theory in the R package ‘RoughSets’,” Information Sciences, Vol.287, pp. 68-89, 2014.
4. [4] R. Bello and R. Falcon: “Rough sets in machine learning: a review,” G. Wang, A. Skowron, Y. Yao, D. Ślęzak, and L. Polkowski eds., in Thriving Rough Sets, Studies in Computational Intelligence, Vol.708, pp. 87-118, Springer, 2017.
5. [5] P. Lingras and C. West: “Interval set clustering of Web users with rough K-means,” J. of Intelligent Information Systems, Vol.23, No.1, pp. 5-16, 2004.
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