Abstract
Rough sets theory depending upon deterministic information has recently been applied to machine learning, knowledge discovery and knowledge acquisition. For handling some incomplete information, we are now discussing rough sets on non-deterministic information and we have developed some tool programs. In this paper, we propose a definition for dependencies of attributes on non-deterministic information and an algorithm for checking it. According to this algorithm, we have realized a program. To clarify the dependency on non-deterministic information will be useful for extraction of rules from non-deterministic information.
This is a preview of subscription content, log in via an institution to check access.
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
Z. Pawlak: Rough Sets, Kluwer Academic Publisher, 1991.
Z. Pawlak: Data versus Logic A Rough Set View, Proc. 4th Int’l. Workshop on Rough Set, Fuzzy Sets and Machine Discovery, pp.1–8, 1996.
E. Orlowska and Z. Pawlak: Logical Foundations of Knowledge Representation, Pas Reports, 537, 1984.
A. Nakamura, S. Tsumoto, H. Tanaka and S. Kobayashi: Rough Set Theory and Its Applications, Journal of Japanese Society for AI, Vol.11, No.2, pp.209–215, 1996.
J. Grzymala-Busse: A New Version of the Rule Induction System LERS, Fundamenta Informaticae, Vol.31, pp.27–39, 1997.
J. Komorowski and J. Zytkow (Eds.): Principles of Data Mining and Knowledge Discovery, Lecture Notes in AI, Vol.1263, 1997.
Z. Ras and S. Joshi: Query Approximate Answering System for an Incomplete DKBS, Fundamenta Informaticae, Vol.30, pp.313–324, 1997.
S. Tsumoto: PRIMEROSE, Bulletin of Int’l. Rough Set Society, Vol.2, No.l, pp.42–43, 1998.
N. Zhong, J. Dong, S. Fujitsu and S. Ohsuga: Soft Techniques to Rule Discovery in Data, Transactions of Information Processing Society of Japan, Vol.39, No.9, pp.2581–2592, 1998.
H. Sakai: Some Issues on Nondeterministic Knowledge Bases with Incomplete and Selective Information, Proc. 1st Int’l. Conf. on Rough Sets and Current Trend of Computing, Lecture Notes in AI, Vol.1424, pp.424–431, 1998.
Sakai and A. Okuma: An Algorithm for Finding Equivalence Relations from Tables with Non-deterministic Information, Proc. 7th Int’l. Conf. on Rough Sets, Fuzzy Sets, Data Mining and Granular-Soft Computing, Lecture Notes in AI, Springer-Verlag, Vol.1711, pp.64–72, 1999.
A. Agrawal, T. Imielinski and A. Swami: A Database Mining, IEEE Trans, on Knowledge and Data Engineering, Vol.5, No.6, pp.914–925, 1993.
J. Quinlan: Introduction of Decision Trees, Machine Learning, Vol.1, pp.81–106, 1986.
W. Lipski: On Semantic Issues Connected with Incomplete Information Data base, ACM Transaction on Database Systems, Vol.4, pp.269–296, 1979.
A. Skowron and J. Grzymala-Busse: From Rough Sets Theory to Evidence Theory, Advances in the Dempster-Shafer Theory of Evidence, John Wiley, pp. 193–236, 1994.
R. Haralick: The Constraint Labeling Problems, IEEE Trans. PAMI, Vol.1, No.2, pp.173–184, 1979.
S. Nishihara: Fundamentals and Perspectives of Constraint Satisfaction Problems, Journal of Japanese Society for AI, Vol.12, No.3, pp.351–358, 1997.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sakai, H., Okuma, A. (2000). An Algorithm for Checking Dependencies of Attributes in a Table with Non-deterministic Information: A Rough Sets Based Approach. In: Mizoguchi, R., Slaney, J. (eds) PRICAI 2000 Topics in Artificial Intelligence. PRICAI 2000. Lecture Notes in Computer Science(), vol 1886. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44533-1_25
Download citation
DOI: https://doi.org/10.1007/3-540-44533-1_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67925-7
Online ISBN: 978-3-540-44533-3
eBook Packages: Springer Book Archive