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All issues Volume 252 (2019) MATEC Web Conf., 252 (2019) 03019 References
Open Access
Issue
MATEC Web Conf.
Volume 252, 2019
III International Conference of Computational Methods in Engineering Science (CMES’18)
Article Number 03019
Number of page(s) 7
Section Computational Artificial Intelligence
DOI https://doi.org/10.1051/matecconf/201925203019
Published online 14 January 2019
  1. Z. Pawlak. Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishing, Dordrecht, 1991 [Google Scholar]
  2. W. Dauben, Joseph. (2005), Georg Cantor, paper on the Foundations of A General Set Theory (1883), 600-612 [Google Scholar]
  3. Ziarko W. (2005) Probabilistic Rough Sets. In: Ślęzak D., Wang G., Szczuka M., Düntsch I., Yao Y. (Eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2005. Lecture Notes in Computer Science, vol 3641. Springer, Berlin, Heidelberg [Google Scholar]
  4. D. Dubois, H. Prade, Rough fuzzy sets and fuzzy rough sets, International Journal of General Systems 17 (1990) 191–209 [CrossRef] [Google Scholar]
  5. D. Dubois, H. Prade, Putting rough sets and fuzzy sets together, in: R. Slowinski (Ed.), Intelligent Decision Support: Handbook ofApplications and Advances of the Sets Theory, Kluwer, Dordrecht, 1992, pp. 203–232 [CrossRef] [Google Scholar]
  6. Greco, S., Matarazzo, B., Słowiński, R.: Rough sets theory for multi-criteria decision analysis. European Journal of Operational Research, 129, 1 (2001) 1–47 [CrossRef] [Google Scholar]
  7. Greco, S., Matarazzo, B., Słowiński, R.: Multicriteria classification by dominance-based rough set approach. In: W. Kloesgen and J. Zytkow (Eds.), Handbook of Data Mining and Knowledge Discovery, Oxford University Press, New York, 2002 [Google Scholar]
  8. Słowiński, R., Greco, S., Matarazzo, B.: Rough set based decision support. Chapter 16 [in]: E.K. Burke and G. Kendall (Eds.), Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, Springer-Verlag, New York (2005) 475–527 [Google Scholar]
  9. Greco, S., B. Matarazzo, R. Slowinski and J. Stefanowski: Variable consistency model of dominance-based rough set approach. In W. Ziarko, Y. Yao (Eds.): Rough Sets and Current Trends in Computing. Lecture Notes in Artificial Intelligence 2005 (2001) 170–181. Springer-Verlag [CrossRef] [Google Scholar]
  10. J.G. Bazan, M. Szczuka, ”The Rough Set Exploration System”, Transactions on Rough Sets III, ser. LNCS, vol. 3400, Springer, Heidelberg, 2005, pp. 37-56. [CrossRef] [Google Scholar]
  11. M. Sudha, A. Kumaravel, Performance Comparison based on Attribute Selection Tools for Data Mining, Indian Journal of Science and Technology, Vol 7, November 2014, pp. 61–65 [Google Scholar]
  12. M. Mohamad, A. Selamat (2018), An Analysis of Rough Set-Based Application Tools in the Decision-Making Process, in: Saeed F., Gazem N., Patnaik S., Saed Balaid A., Mohammed F. (Eds) Recent Trends in Information and Communication Technology. IRICT 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 5. Springer, Cham [Google Scholar]
  13. C.R. Kavitha, T. Mahalekshmi, Performance Comparison based on Attribute Selection Techniques of WEKA and ROSE Tools, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 4, October 2016 [Google Scholar]
  14. A. Janusz, M. Szczuka, S. Stawicki, D. Ślęzak, Rough Set Tools for Practical Data Exploration, Proc: Rough Sets and Knowledge Technology: 10th International Conference, RSKT 2015, Held as Part of the International Joint Conference on Rough Sets, IJCRS 2015, Tianjin, China, November 20-23, 2015, Proceedings, pp.77-86 [Google Scholar]
  15. L.S. Riza, A. Janusz., C. Bergmeir, C. Cornelis, F. Herrera, D. Ślezak, J. M. Benítez (2014), Implementing algorithms of rough set theory and fuzzy rough set theory in the R package “RoughSets”, Inf. Sci., 287, pp. 68-89 [CrossRef] [Google Scholar]
  16. Zain Abbas, Aqil Burney, A Survey of Software Packages Used for Rough Set Analysis, Journal of Computer and Communications, 2016, vol. 4, pp. 10-18 [CrossRef] [Google Scholar]
  17. Alekhya Cherukri, Madhuri Doguparthi, Comprehensive Analysis of various rough set tools for data mining, International Journal of Advances in Electronics and Computer Science, Vol. 4, April 2017 [Google Scholar]
  18. Pięta P., Analysis of data mining algorthms based on Rough Sets theory, Master Thesis (Supervisor : Prof. Tomasz Szmuc), AGH University of Science and Technology, Cracow, Poland, 2018 [Google Scholar]
  19. J. Komorowski, Z. Pawlak., L. Polkowski, A. Skowron, Rough Sets: A tutorial, 1997 [Google Scholar]
  20. RSES_doc_eng.pdf, User Guide’s [Google Scholar]
  21. J. G. Bazan, M. Szczuka, J. Wróblewski (2002) A New Version of Rough Set Exploration System, In: Alpigini J.J., Peters J.F., Skowron A., Zhong N. (Eds) Rough Sets and Current Trends in Computing. RSCTC 2002. Lecture Notes in Computer Science, vol 2475. Springer [Google Scholar]
  22. http://logic.mimuw.edu.pl/~rses/ [Google Scholar]
  23. http://idss.cs.put.poznan.pl/site/rose.html [Google Scholar]
  24. B. Predki, R. Slowinski, J. Stefanowski, R. Susmaga, S. Wilk: ROSE Software Implementation of the Rough Set Theory. In: L. Polkowski, A. Skowron, Eds. Rough Sets and Current Trends in Computing, Lecture Notes in Artificial Intelligence, vol. 1424. Springer-Verlag, Berlin (1998), 605-608 [Google Scholar]
  25. B. Predki, S. Wilk: Rough Set Based Data Exploration Using ROSE System. In: Z.W. Ras, A. Skowron, Eds. Foundations of Intelligent Systems, Lecture Notes in Artificial Intelligence, vol. 1609. Springer-Verlag, Berlin (1999), 172-180 [Google Scholar]
  26. A. Øhrn, J. Komorowski (1997), ROSETTA: A Rough Set Toolkit for Analysis of Data, Proc. Third International Joint Conference on Information Sciences, Fifth International Workshop on Rough Sets and Soft Computing (RSSC’97), Durham, NC, USA, March 1-5, Vol. 3, pp. 403-407 [Google Scholar]
  27. A. Øhrn, J. Komorowski, A. Skowron, P. Synak, The Design and Implementation of a Knowledge Discovery Toolkit Based on Rough Sets: The ROSETTA System, Rough Sets in Knowledge Discovery 1: Methodology and Applications, L. Polkowski and A. Skowron (Eds.), Studies in Fuzziness and Soft Computing, Vol. 18, Chapter 19, pp. 376-399 [Google Scholar]
  28. A. Øhrn (2000), ROSETTA Technical Reference Manual, Department of Computer and Information Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway [Google Scholar]
  29. http://bioinf.icm.uu.se/rosetta/downloads.php [Google Scholar]
  30. R. Jensen, Tutorial on fuzzy-rough data mining using Weka, http://users.aber.ac.uk/rkj/Weka.pdf [Google Scholar]
  31. http://users.aber.ac.uk/rkj/book/wekafull.jar [Google Scholar]
  32. J. Błaszczyński, S. Greco, B. Matarazzo, R. Słowiński, M. Szeląg, jMAF Dominance-Based Rough Set Data Analysis Framework, Rough Sets and Intelligent Systems Professor Zdzisław Pawlak in Memoriam, Springer, 2012 [Google Scholar]
  33. http://www.cs.put.poznan.pl/jblaszczynski/Site/jRS.html [Google Scholar]

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