Abstract
We have investigated rough set-based concepts for a given Non-deterministic Information System (NIS). In this paper, we consider generating a NIS from a Deterministic Information System (DIS) intentionally. A NIS \(\varPhi\) is seen as a diluted DIS ϕ, and we can hide the actual values in ϕ by using \(\varPhi\). We name this way of hiding Information Dilution by non-deterministic information. This paper considers information dilution and its application to hiding the actual values in a table.
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References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proc. of VLDB, pp. 487–499 (1994)
Aggarwal, C., Yu, P.: Privacy-Preserving Data Mining. In: Advances in Database Systems, vol. 34. Springer (2008)
Grzymała-Busse, J.: A new version of the rule induction system LERS. Fundamenta Informaticae 31, 27–39 (1997)
Grzymała-Busse, J., Rząsa, W.: A local version of the MLEM2 algorithm for rule induction. Fundamenta Informaticae 100, 99–116 (2010)
Lipski, W.: On semantic issues connected with incomplete information data base. ACM Trans. DBS 4, 269–296 (1979)
Lipski, W.: On databases with incomplete information. Journal of the ACM 28, 41–70 (1981)
Nakata, M., Sakai, H.: Twofold rough approximations under incomplete information. International Journal of General Systems 42(6), 546–571 (2013)
Orłowska, E., Pawlak, Z.: Representation of nondeterministic information. Theoretical Computer Science 29, 27–39 (1984)
Pawlak, Z.: Rough Sets. Kluwer Academic Publishers (1991)
RNIA software logs, http://www.mns.kyutech.ac.jp/~sakai/RNIA
Sakai, H., Ishibashi, R., Nakata, M.: On rules and apriori algorithm in non-deterministic information systems. Transactions on Rough Sets 9, 328–350 (2008)
Sakai, H., Okuma, H., Nakata, M.: Rough non-deterministic information analysis: Foundations and its perspective in machine learning. In: Smart Innovation, Systems and Technologies, ch. 9, vol. 13, pp. 215–247. Springer (2013)
Sakai, H., Okuma, H., Wu, M., Nakata, M.: Rough non-deterministic information analysis for uncertain information. In: The Handbook on Reasoning-Based Intelligent Systems, ch. 4, pp. 81–118. World Scientific (2013)
Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Intelligent Decision Support. Handbook of Advances and Applications of the Rough Set Theory, pp. 331–362. Kluwer Academic Publishers (1992)
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Sakai, H., Wu, M., Yamaguchi, N., Nakata, M. (2013). Rough Set-Based Information Dilution by Non-deterministic Information. In: Ciucci, D., Inuiguchi, M., Yao, Y., Ślęzak, D., Wang, G. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2013. Lecture Notes in Computer Science(), vol 8170. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41218-9_7
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DOI: https://doi.org/10.1007/978-3-642-41218-9_7
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