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A Parallel Approximate Rule Extracting Algorithm Based on the Improved Discernibility Matrix

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Rough Sets and Current Trends in Computing (RSCTC 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3066))

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Abstract

A parallel rule-extracting algorithm based on the improved discernibility matrix [2] is proposed, by this way, a large amount of raw data can be divided into some small portions to be processed in parallel. The confidence factor is also introduced to the rule sets to obtain the uncertainty rules. The most important advantage of this algorithm is that it does not need to calculate the discernibility matrix corresponding to these overall data.

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References

  1. Pawlak, Z.: Rough sets. International Journal of Computer and Information Science 11(5), 341–356 (1982)

    Article  MATH  MathSciNet  Google Scholar 

  2. Bazan, J.G., Nguyen, H.S., Nguyen, S.H., Synak, P., Wroblewski, J.: Rough Set Algorithms in Classification Problem. In: Polkowski, L., Tsumoto, S., Lin, T.Y. (eds.) Rough Set Methods and Applications, pp. 49–88. Physica-Verlag, Heidelberg (2000)

    Google Scholar 

  3. Pawlak, Z., Grzymala-Busse, J., Slowinski, R.: Rough sets. Communications of the ACM 8(11), 89–95 (1995)

    Google Scholar 

  4. Pawlak, Z.: Rough sets: theoretical aspects and reasoning about data. Kluwer Academic Publishers, Dordrecht (1991)

    MATH  Google Scholar 

  5. Pawlak, Z.: On learning - a rough set approach. In: Goos, G., et al. (eds.) Proceedings of International Symposium on Computation Theory. LNCS, vol. 208, pp. 197–227 (1984)

    Google Scholar 

  6. Skowron, A.: The Rough sets theory and evidence theory. Fundamenta Informaticae 13, 245–262 (1990)

    MATH  MathSciNet  Google Scholar 

  7. Shan, N., Ziarko, W.: An incremental learning algorithm for constructing decision ruls. In: Kluwer, R.S. (ed.) Rough Sets, Fuzzy Sets and Knowledge Discovery, pp. 326–334. Springer, Heidelberg (1994)

    Google Scholar 

  8. Bian, X.: Certain rule learning of the inconsistent data. Journal of East China Shipbuilding Institute 12(1), 25–30 (in Chinese)

    Google Scholar 

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© 2004 Springer-Verlag Berlin Heidelberg

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Yong, L., Congfu, X., Yunhe, P. (2004). A Parallel Approximate Rule Extracting Algorithm Based on the Improved Discernibility Matrix. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds) Rough Sets and Current Trends in Computing. RSCTC 2004. Lecture Notes in Computer Science(), vol 3066. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25929-9_60

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  • DOI: https://doi.org/10.1007/978-3-540-25929-9_60

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22117-3

  • Online ISBN: 978-3-540-25929-9

  • eBook Packages: Springer Book Archive

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