Abstract
This paper advances rule generation in Lipski’s incomplete information databases, and develops a software tool for rule generation. We focus on three kinds of information incompleteness. The first is non-deterministic information, the second is missing values, and the third is intervals. For intervals, we introduce the concept of a resolution. Three kinds of information incompleteness are uniformly handled by NIS-Apriori algorithm. An overview of a prototype system in Prolog is presented.
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References
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Proc. of VLDB, pp. 487–499 (1994)
Dembczyński, K., Greco, S., Słowiński, R.: Rough Set Approach to Multiple Criteria Classification with Imprecise Evaluations and Assignments. European J. Operational Research 198, 626–636 (2009)
Grzymała-Busse, J.: Data with Missing Attribute Values: Generalization of Indiscernibility Relation and Rule Induction. Transactions on Rough Sets 1, 78–95 (2004)
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)
Orłowska, E., Pawlak, Z.: Representation of Nondeterministic Information. Theoretical Computer Science 29, 27–39 (1984)
Pawlak, Z.: Rough Sets. Kluwer Academic Publishers, Dordrecht (1991)
Sakai, H., Okuma, A.: Basic Algorithms and Tools for Rough Non-deterministic Information Analysis. Transactions on Rough Sets 1, 209–231 (2004)
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., Nakata, M., Ślęzak, D.: Rule Generation in Lipski’s Incomplete Information Databases. In: Szczuka, M., Kryszkiewicz, M., Ramanna, S., Jensen, R., Hu, Q. (eds.) RSCTC 2010. LNCS, vol. 6086, pp. 376–385. Springer, Heidelberg (2010)
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, Dordrecht (1992)
Zadeh, L.A.: Toward a Theory of Fuzzy Information Granulation and its Centrality in Human Reasoning and Fuzzy Logic. Fuzzy Sets and Systems 90, 111–127 (1997)
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Sakai, H., Nakata, M., Ślęzak, D. (2011). A Prototype System for Rule Generation in Lipski’s Incomplete Information Databases. In: Kuznetsov, S.O., Ślęzak, D., Hepting, D.H., Mirkin, B.G. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2011. Lecture Notes in Computer Science(), vol 6743. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21881-1_29
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DOI: https://doi.org/10.1007/978-3-642-21881-1_29
Publisher Name: Springer, Berlin, Heidelberg
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