Skip to main content

On New Concept in Computation of Reduct in Rough Sets Theory

  • Conference paper
Rough Sets and Knowledge Technology (RSKT 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5589))

Included in the following conference series:

Abstract

A new concept of Reduct computation is proposed. This set is referred as NewReduct. It is discovered by defining the Indiscernibility matrix modulo (iDMM D) and Indiscernibility function modulo(iDFM D). Reduct is known as interesting and important set of attributes that able to represent the IS, in adverse the NewReduct is set of superfluous, redundant and non-interesting attributes. The computation of attributes defines sets of dispensable attributes and the partitioning of the objects based on indiscernibility relations shaped the information of a new knowledge. It is assumed that the sets of NewReduct attributes are able to uncover hidden knowledge that lies under a hidden pattern. One important knowledge that may be discovered from the IS or DS is the outliers knowledge.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight 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. Pawlak, Z.: Rough Sets. Int. Journal of Computer and Information Sciences 11(5), 341–356 (1982)

    Article  MATH  Google Scholar 

  2. Mollestad, T.: A Rough Set Aproach to Data Mining: Extracting a Logic of Default Rules from Data, The Norwegian University of Science and Technology (1997)

    Google Scholar 

  3. Mollestad, T., Komorowski, J.: A Rough Set Framework of Prepositional for Mining Default Rules. Springer, Singapore (1998)

    Google Scholar 

  4. Bakar, A.A., Sulaiman, M.N., Othman, M., Selamat, M.H.: IP Algorithms in Compact Rough Classification Modeling. Intelligent Data Analysis 5(5), 419–429 (2001)

    MATH  Google Scholar 

  5. Polkowski, L.: Advances in Soft Computing, Rough Sets Mathematical Foundations. Springer, Heidelberg (2002)

    MATH  Google Scholar 

  6. Pawlak, Z., Grzymala-Busse, J., Slowinski, R., Ziarko, W.: Rough Sets. Communication of the ACM [Electronic Version] 38 (1995)

    Google Scholar 

  7. Polkowski, L.: Advances in Soft Computing, Rough Sets Mathematical Foundations. Springer, Heidelberg (2002)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Shaari, F., Abu Bakar, A., Hamdan, A.R. (2009). On New Concept in Computation of Reduct in Rough Sets Theory. In: Wen, P., Li, Y., Polkowski, L., Yao, Y., Tsumoto, S., Wang, G. (eds) Rough Sets and Knowledge Technology. RSKT 2009. Lecture Notes in Computer Science(), vol 5589. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02962-2_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-02962-2_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02961-5

  • Online ISBN: 978-3-642-02962-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics