Abstract
Incremental learning is an efficient technique for knowledge discovery in a dynamic database. Rough set theory is an important mathematical tool for data mining and knowledge discovery in information systems. The lower and upper approximations in the rough set theory may change while data in the information system evolves with time. In this paper, we focus on the incremental updating principle for computing approximations in set-valued ordered information systems. The approaches for updating approximations are proposed when the object set varies over time.
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References
Zadeh, L.A.: Towards a Theory of Fuzzy Information Granulation and Its Centrality in Human Reasoning and Fuzzy Logic. Fuzzy Sets and Systems 90(2), 111–127 (1997)
Zadeh, L.A.: Fuzzy Logic=Computing with Words. IEEE Tran. On Fuzzy Systems 4(1), 103–111 (1996)
Yao, Y.Y., Zhong, N.: Potential Applications of Granular Computing in Knowledge Discovery and Data Mining. In: Proc. World Multiconference on Systemics Cybernetics and Informatics, pp. 573–580 (1999)
Yao, Y.Y.: Perspectives of Granular Computing. In: Proc. GrC, pp. 85–90 (2005)
Pawlak, Z.: Rough Sets. International Journal of Computer and Information Sciences 11, 341–356 (1982)
Yao, Y.Y.: Granular Computing: Basic Issues and Possible Solutions. In: Proc. 5th Joint Conference on Information Sciences, pp. 186–189 (2000)
Pedrycz, W., Weber, R.: Special Issue on Soft Computing for Dynamic Data Mining. Applied Soft Computing 8(4), 1281–1282 (2008)
Shan, N., Ziarko, W.: Data-based acquisition and incremental modification of classification rules. Computational Intelligence 11(2), 357–370 (1995)
Liu, D., Li, T., Ruan, D., Zou, W.: An incremental approach for inducing knowledge from dynamic information systems. Fundamenta Informaticae 94(2), 245–260 (2009)
Li, T.R., Ruan, D., Geert, W., et al.: A Rough Sets Based Characteristic Relation Approach for Dynamic Attribute Generalization in Data Mining. Knowledge-Based Systems 20, 485–494 (2007)
Li, T.R., Ruan, D., Song, J.: Dynamic Maintenance of Decision Rules with Rough Set under Characteristic Relation. Wireless Communications Networking and Mobile Computing, 3713–3716 (2007)
Chen, H.M., Li, T.R., Qiao, S.J., Ruan, D.: A Rough Set Based Dynamic Maintenance Approach for Approximations in Coarsening and Refining Attribute Values. International Journal of Intelligent Systems 25(10), 1005–1026 (2010)
Chen, Y.N., Tseng, T.L., Chen, C.C., Huang, C.C.: Rule Induction Based on an Incremental Rough Set. Expert Systems with Applications 36(9), 11439–11450 (2009)
Guan, Y., Wang, H.: Set-valued information systems. Information Sciences 176(17), 2507–2525 (2006)
Qian, Y.H., Dang, C.Y., Liang, J.Y., Tang, D.W.: Set-valued ordered information systems. Information Sciences 179(16), 2809–2832 (2009)
Chen, H.M., Li, T.R., Zhang, J.B.: A method for incremental updating approximations based on variable precision set-valued ordered information systems. In: Proc. GrC, pp. 96–101 (2010)
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© 2012 Springer-Verlag Berlin Heidelberg
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Luo, C., Li, T., Chen, H., Liu, D. (2012). An Incremental Approach for Updating Approximations Based on Set-Valued Ordered Information Systems. In: Yao, J., et al. Rough Sets and Current Trends in Computing. RSCTC 2012. Lecture Notes in Computer Science(), vol 7413. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32115-3_43
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DOI: https://doi.org/10.1007/978-3-642-32115-3_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32114-6
Online ISBN: 978-3-642-32115-3
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