Knowledge Reduction Based on Granular Computing from Decision Information Systems
Efficient knowledge reduction in large inconsistent decision information systems is a challenging problem. Moreover, existing approaches have still their own limitations. To address these problems, in this article, by applying the technique of granular computing, provided some rigorous and detailed proofs, and discussed the relationship between granular reduct introduced and knowledge reduction based on positive region related to simplicity decision information systems. By using radix sorting and hash methods, the object granules as basic processing elements were employed to investigate knowledge reduction. The proposed method can be applied to both consistent and inconsistent decision information systems.
KeywordsGranular computing Rough set theory Knowledge reduction Decision information systems Granular
Unable to display preview. Download preview PDF.
- 1.Bargiela, A., Pedrycz, W.: Granular Computing: An Introduction. Kluwer Academic Publishers, Dordrecht (2002)Google Scholar
- 2.Miao, D.Q., Wang, G.Y., Liu, Q., Lin, T.Y., Yao, Y.Y.: Granular Computing: Past, Present, and the Future Perspectives. Academic Press, Beijing (2007)Google Scholar
- 6.Lin, T.Y., Louie, E.: Finding Association Rules by Granular Computing: Fast Algorithms for Finding Association Rules. In: Proceedings of the 12th International Conference on Data Mining, Rough Sets and Granular Computing, Berlin, German, pp. 23–42 (2002)Google Scholar
- 9.Xu, Z.Y., Liu, Z.P., et al.: A Quick Attribute Reduction Algorithm with Complexity of Max(O(|C||U|),O(|C| 2|U/C|)). Journal of Computers 29(3), 391–399 (2006)Google Scholar
- 10.Liu, Y., Xiong, R., Chu, J.: Quick Attribute Reduction Algorithm with Hash. Chinese Journal of Computers 32(8), 1493–1499 (2009)Google Scholar
- 11.Liu, S.H., Sheng, Q.J., Wu, B., et al.: Research on Efficient Algorithms for Rough Set Methods. Chinese Journal of Computers 26(5), 524–529 (2003)Google Scholar