Abstract
We have been coping with issues connected with non-deterministic information in rough sets. Non-deterministic information is a kind of incomplete information, and it defines a set in which the actual value exists, but we do not know which is the actual value. If the defined set is equal to the domain of attribute values, we may see this is corresponding to a missing value. We need to pick up the merits in each information, and need to apply them to analyzing data sets. In this paper, we describe our opinion on non-deterministic information as well as incomplete information, some algorithms, software tools, and its perspective in rough sets.
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References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proc. of VLDB, pp. 487–499 (1994)
Ciucci, D., Flaminio, T.: Generalized rough approximations in PI 1/2. Int. Journal of Approximate Reasoning 48(2), 544–558 (2008)
Codd, E.: A relational model of data for large shared data banks. Communication of the ACM 13, 377–387 (1970)
Demri, S., Orłowska, E.: Incomplete Information: Structure, Inference, Complexity. Monographs in Theoretical Computer Science. Springer (2002)
Greco, S., Matarazzo, B., Słowiński, R.: Granular computing and data mining for ordered data: The dominance-based rough set approach. In: Encyclopedia of Complexity and Systems Science, pp. 4283–4305 (2009)
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)
Kryszkiewicz, M.: Rules in incomplete information systems. Information Sciences 113, 271–292 (1999)
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)
Marek, W., Pawlak, Z.: Information storage and retrieval systems: Mathematical foundations. Theoretical Computer Science 1(4), 331–354 (1976)
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. International Journal of Computer and Information Sciences 11, 341–356 (1982)
Pawlak, Z.: Systettly Informacyjne. Podstawy Teoretyczne (Information Systems. Theoretical Foundations). WNT, Warsaw (1983)
Pawlak, Z.: Rough Sets. Kluwer Academic Publishers (1991)
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.: RNIA software logs (2011), http://www.mns.kyutech.ac.jp/~sakai/RNIA
Sakai, H., Okuma, H., Nakata, M.: Rough non-deterministic information analysis: Foundations and its perspective in machine learning. In: Ramanna, S., Jain, L.C., Howlett, R.J. (eds.) Emerging Paradigms in ML. SIST, vol. 13, pp. 215–247. Springer, Heidelberg (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)
Ślęzak, D., Eastwood, V.: Data warehouse technology by infobright, Proc. In: SIGMOD Conference, pp. 841–846 (2009)
Ślęzak, D., Sakai, H.: Automatic extraction of decision rules from non-deterministic data systems: Theoretical foundations and SQL-based implementation. In: Ślęzak, D., Kim, T.-H., Zhang, Y., Ma, J., Chung, K.-I. (eds.) DTA 2009. CCIS, vol. 64, pp. 151–162. Springer, Heidelberg (2009)
UCI Machine Learning Repository, http://mlearn.ics.uci.edu/MLRepository.html
Wu, M., Sakai, H.: getRNIA web software (2013), http://getrnia.appspot.com/
Yao, Y., Zhao, Y.: Attribute reduction in decision-theoretic rough set models. Information Sciences 178(17), 3356–3373 (2008)
Zhu, W.: Topological approaches to covering rough sets. Information Sciences 177(6), 1499–1508 (2007)
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Sakai, H., Wu, M., Yamaguchi, N., Nakata, M. (2013). Non-deterministic Information in Rough Sets: A Survey and Perspective. In: Lingras, P., Wolski, M., Cornelis, C., Mitra, S., Wasilewski, P. (eds) Rough Sets and Knowledge Technology. RSKT 2013. Lecture Notes in Computer Science(), vol 8171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41299-8_2
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DOI: https://doi.org/10.1007/978-3-642-41299-8_2
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