Approaches for Updating Approximations in Set-Valued Information Systems While Objects and Attributes Vary with Time

Part of the Intelligent Systems Reference Library book series (ISRL, volume 42)

Abstract

Rough set theory is an important tool for knowledge discovery. The lower and upper approximations are basic operators in rough set theory. Certain and uncertain if-then rules can be unrevealed from different regions partitioned by approximations. In real-life applications, data in the information system are changing frequently, for example, objects, attributes, and attributes’ values in the information system may vary with time. Therefore, approximations may change over time. Updating approximations efficiently is crucial to the knowledge discovery. The set-valued information system is a general model of the information system. In this chapter, we focus on studying principles for incrementally updating approximations in a set-valued information system while attributes and objects are added. Then, methods for updating approximations of a concept in a set-valued information system is given while attributes and objects change simultaneously. Finally, an extensive experimental evaluation verifies the effectiveness of the proposed method.

Keywords

Knowledge discovery rough set theory set-valued information system approximations 

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References

  1. 1.
    Bilski, P., Wojciechowski, J.M.: Rough set approach to knowledge-based decision support. European Journal of Operational Research 99(1), 48–57 (1997)CrossRefGoogle Scholar
  2. 2.
    Błaszczyński, J., Greco, S., Słowiński, R., Szeląg, M.: Monotonic variable consistency rough set approaches. International Journal of Approximate Reasoning 50(7), 979–999 (2009)MathSciNetMATHCrossRefGoogle Scholar
  3. 3.
    Chen, H.M., Li, T.R., Qiao, S.J., Ruan, D.: A rough sets based dynamic maintenance approach for approximations in coarsening and refining attribute values. International Journal of Intelligent Systems 25, 1005–1026 (2010)MATHCrossRefGoogle Scholar
  4. 4.
    Chen, H.M., Li, T.R., Zhang, J.B.: A Method for incremental updating approximations when objects and attributes vary with time. In: Proceedings of 2010 IEEE International Conference on Granular Computing, Silicon Valley, pp. 90–95. IEEE Computer Society Press, Washington, DC (2010)CrossRefGoogle Scholar
  5. 5.
    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: Proceedings of the 2010 IEEE International Conference on Granular Computing, Silicon Valley, pp. 96–101. IEEE Computer Society Press, Washington, DC (2010)CrossRefGoogle Scholar
  6. 6.
    Cheng, Y.: The incremental method for fast computing the rough fuzzy approximations. Data & Knowledge Engineering 70(1), 84–100 (2011)CrossRefGoogle Scholar
  7. 7.
    Ciucci, D.: Classification of Dynamics in Rough Sets. In: Szczuka, M., Kryszkiewicz, M., Ramanna, S., Jensen, R., Hu, Q. (eds.) RSCTC 2010. LNCS, vol. 6086, pp. 257–266. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Ciucci, D.: Temporal Dynamics in Rough Sets Based on Coverings. In: Yu, J., Greco, S., Lingras, P., Wang, G., Skowron, A. (eds.) RSKT 2010. LNCS, vol. 6401, pp. 126–133. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Ciucci, D.: Attribute Dynamics in Rough Sets. In: Kryszkiewicz, M., Rybinski, H., Skowron, A., Raś, Z.W. (eds.) ISMIS 2011. LNCS, vol. 6804, pp. 43–51. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  10. 10.
    Fan, Y.N., Tseng, T.L., Chern, C.C., Huang, C.C.: Rule induction based on an incremental rough set. Expert Systems with Applications 36(9), 11439–11450 (2009)CrossRefGoogle Scholar
  11. 11.
    Guan, Y.Y., Wang, H.K.: Set-valued information systems. Information Sciences 176(17), 2507–2525 (2006)MathSciNetMATHCrossRefGoogle Scholar
  12. 12.
    Deng, D., Huang, H.-K.: Dynamic Reduction Based on Rough Sets in Incomplete Decision Systems. In: Yao, J., Lingras, P., Wu, W.-Z., Szczuka, M.S., Cercone, N.J., Ślęzak, D. (eds.) RSKT 2007. LNCS (LNAI), vol. 4481, pp. 76–83. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Hassanien, A.E., Abraham, A., Peters, J.F., Schaefer, G., Henry, C.: Rough sets and near sets in medical imaging: A review. IEEE Transactions on Information Technology in Biomedicine 13(6), 955–968 (2009)CrossRefGoogle Scholar
  14. 14.
    Kryszkiewicz, M.: Rough set approach to incomplete. Information Sciences 112, 39–49 (1998)MathSciNetMATHCrossRefGoogle Scholar
  15. 15.
    Liu, D., Li, T.R., Ruan, D., Zou, W.L.: An incremental approach for inducing knowledge from dynamic information systems. Fundamenta Informaticae 94, 245–260 (2009)MathSciNetMATHGoogle Scholar
  16. 16.
    Li, T.R., Ruan, D., Wets, G., Song, J., Xu, Y.: A rough sets based characteristic relation approach for dynamic attribute generalization in data mining. KnowledgeBased Systems 20(5), 485–494 (2007)CrossRefGoogle Scholar
  17. 17.
    Pattaraintakorn, P., Cercone, N.: Integrating rough set theory and medical applications. Applied Mathematics Letters 21(4), 400–403 (2008)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Pawlak, Z.: Rough sets. International Journal of Computer and Information Sciences 11, 341–356 (1982)MathSciNetMATHCrossRefGoogle Scholar
  19. 19.
    Pawlak, Z.: Rough set approach to knowledge-based decision support. European Journal of Operational Research 99(1), 48–57 (1997)MATHCrossRefGoogle Scholar
  20. 20.
    Qian, Y.H., Liang, J.Y., Pedrycz, W., Dang, C.Y.: Positive approximation: An accelerator for attribute reduction in rough set theory. Artificial Intelligence 174(9-10), 597–618 (2010)MathSciNetMATHCrossRefGoogle Scholar
  21. 21.
    Qian, Y.H., Dang, C.Y., Liang, J.Y., Tang, D.W.: Set-valued ordered information systems. Information Sciences 179(16), 2809–2832 (2009)MathSciNetMATHCrossRefGoogle Scholar
  22. 22.
    Skowron, A., Stepaniuk, J., Swiniarski, R.: Approximation spaces in rough-granular computing. Fundamenta Informaticae 100, 141–157 (2010)MathSciNetMATHGoogle Scholar
  23. 23.
    Song, X.X., Zhang, W.X.: Knowledge reduction in inconsistent set-valued decision information system. Computer Engineering and Applications 45(1), 33–35 (2009)Google Scholar
  24. 24.
    Stefanowski, J., Tsoukiàs, A.: On the Extension of Rough Sets under Incomplete Information. In: Zhong, N., Skowron, A., Ohsuga, S. (eds.) RSFDGrC 1999. LNCS (LNAI), vol. 1711, pp. 73–82. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  25. 25.
    Swiniarski, R.W., Pancerz, K., Suraj, Z.: Prediction of model changes of concurrent systems described by temporal information systems. In: Hamid, R. (ed.) Proceedings of the 2005 International Conference on Data Mining, Las Vegas, Nevada, USA, pp. 51–57. CSREA Press (2005)Google Scholar
  26. 26.
    Wang, G.Y.: Extension of rough set under incomplete information systems. Journal of Computer Research and Development 39(10), 1238–1243 (2002)Google Scholar
  27. 27.
    Yao, Y.Y.: The superiority of three-way decisions in probabilistic rough set models. Information Sciences 181, 1080–1096 (2011)MathSciNetMATHCrossRefGoogle Scholar
  28. 28.
    Zheng, Z., Wang, G.Y.: RRIA: A rough set and rule tree based incremental knowledge acquisition algorithm. Fundamenta Informaticae 59, 299–313 (2004)MathSciNetMATHGoogle Scholar
  29. 29.
    Zou, W.L., Li, T.R., Chen, H.M., Ji, X.L.: Approaches for incrementally updating approximations based on set-valued information systems while attribute values’ coarsening and refining. In: Proceedings of the 2009 IEEE International Conference on Granular Computing, pp. 824–829. IEEE Computer Society Press, Washington, DC (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.School of Information Science and TechnologySouthwest Jiaotong University & Key Lab of Cloud Computing and Intelligent TechnologyChengduChina
  2. 2.School of Information Science and TechnologySouthwest Jiaotong UniversityChengduChina

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