Skip to main content
SpringerLink
Log in

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

  • Chapter
Rough Sets and Intelligent Systems - Professor Zdzisław Pawlak in Memoriam

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

  • 1415 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bilski, P., Wojciechowski, J.M.: Rough set approach to knowledge-based decision support. European Journal of Operational Research 99(1), 48–57 (1997)

    Article  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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)

    Chapter  Google Scholar 

  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)

    Chapter  Google Scholar 

  6. Cheng, Y.: The incremental method for fast computing the rough fuzzy approximations. Data & Knowledge Engineering 70(1), 84–100 (2011)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  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)

    Chapter  Google Scholar 

  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)

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  11. Guan, Y.Y., Wang, H.K.: Set-valued information systems. Information Sciences 176(17), 2507–2525 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  14. Kryszkiewicz, M.: Rough set approach to incomplete. Information Sciences 112, 39–49 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    MathSciNet  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  17. Pattaraintakorn, P., Cercone, N.: Integrating rough set theory and medical applications. Applied Mathematics Letters 21(4), 400–403 (2008)

    Article  MathSciNet  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  19. Pawlak, Z.: Rough set approach to knowledge-based decision support. European Journal of Operational Research 99(1), 48–57 (1997)

    Article  MATH  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  22. Skowron, A., Stepaniuk, J., Swiniarski, R.: Approximation spaces in rough-granular computing. Fundamenta Informaticae 100, 141–157 (2010)

    MathSciNet  MATH  Google Scholar 

  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. 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)

    Chapter  Google Scholar 

  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. 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. Yao, Y.Y.: The superiority of three-way decisions in probabilistic rough set models. Information Sciences 181, 1080–1096 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  28. Zheng, Z., Wang, G.Y.: RRIA: A rough set and rule tree based incremental knowledge acquisition algorithm. Fundamenta Informaticae 59, 299–313 (2004)

    MathSciNet  MATH  Google Scholar 

  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)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information Science and Technology, Southwest Jiaotong University & Key Lab of Cloud Computing and Intelligent Technology, Sichuan Province, Chengdu, 610031, China

    Hongmei Chen & Tianrui Li

  2. School of Information Science and Technology, Southwest Jiaotong University, Chengdu, 610031, China

    Hongmei Tian

Authors
  1. Hongmei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tianrui Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hongmei Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hongmei Chen .

Editor information

Editors and Affiliations

  1. Institute of Mathematics, Warsaw University, Banacha 2, Warsaw, 02097, Poland

    Andrzej Skowron

  2. Institute of Computer Science, University of Rzeszów, ul. Dekerta 2, Rzeszów, 35-030, Poland

    Zbigniew Suraj

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Chen, H., Li, T., Tian, H. (2013). Approaches for Updating Approximations in Set-Valued Information Systems While Objects and Attributes Vary with Time. In: Skowron, A., Suraj, Z. (eds) Rough Sets and Intelligent Systems - Professor Zdzisław Pawlak in Memoriam. Intelligent Systems Reference Library, vol 42. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30344-9_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-30344-9_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30343-2

  • Online ISBN: 978-3-642-30344-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation