Abstract
Rough Non-deterministic Information Analysis (RNIA) is a rough set based framework for handling several kinds of incomplete information. In our previous research on RNIA, we gave definitions according to two modal concepts, the certainty and the possibility, and thoroughly investigated their mathematical properties. For rule generation in RNIA, we proposed NIS-Apriori algorithm, which is an extended Apriori algorithm. Our previous implementation of NIS-Apriori in C suffered from a lack of clarity caused by difficulties in expressing non-deterministic information by procedural languages. Therefore, we recently decided to improve the algorithm’s design and re-implement it in Prolog. This paper reports the current state of our algorithmic framework and outlines some new aspects of its functionality.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: 20th Very Large Data Base, pp. 487–499 (1994)
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)
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., Ishibashi, R., Koba, K., Nakata, M.: On Rules and Apriori Algorithm in Non-deterministic Information Systems. In: Peters, J.F., Skowron, A., Rybiński, H. (eds.) Transactions on Rough Sets IX. LNCS, vol. 5390, pp. 328–350. Springer, Heidelberg (2008)
Sakai, H., Nakata, M.: An Application of Discernibility Functions to Generating Minimal Rules in Non-deterministic Information Systems. Journal of Advanced Computational Intelligence and Intelligent Informatics 10, 695–702 (2006)
Sakai, H., Nakata, M., Ślęzak, D.: 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.) RSFDGrC 2011. LNCS(LNAI), vol. 6743, pp. 175–182. Springer, Heidelberg (2011)
Sakai, H., Okuma, A.: Basic Algorithms and Tools for Rough Non-deterministic Information Analysis. In: Peters, J.F., Skowron, A., Grzymała-Busse, J.W., Kostek, B.z., Świniarski, R.W., Szczuka, M.S. (eds.) Transactions on Rough Sets I. LNCS, vol. 3100, pp. 209–231. Springer, Heidelberg (2004)
Sakai, H., Okuma, H., Nakata, M., Ślęzak, D.: Stable Rule Extraction and Decision Making in Rough Non-deterministic Information Analysis. International Journal of Hybrid Intelligent Systems 8(1), 41–57 (2011)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sakai, H., Nakata, M., Ślęzak, D. (2011). A NIS-Apriori Based Rule Generator in Prolog and Its Functionality for Table Data. In: Yao, J., Ramanna, S., Wang, G., Suraj, Z. (eds) Rough Sets and Knowledge Technology. RSKT 2011. Lecture Notes in Computer Science(), vol 6954. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24425-4_31
Download citation
DOI: https://doi.org/10.1007/978-3-642-24425-4_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24424-7
Online ISBN: 978-3-642-24425-4
eBook Packages: Computer ScienceComputer Science (R0)