Skip to main content
SpringerLink
Log in

Attribute Reduction and Rule Acquisition of Formal Decision Context Based on Dual Concept Lattice

  • Conference paper
  • First Online:
Learning Technologies and Systems (SETE 2020, ICWL 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12511))

  • 1079 Accesses

Abstract

Concept lattice theory is a powerful tool for analyzing and extracting information from data sets. Rule acquisition and attribute reduction are hot research topics in formal concept analysis. This paper mainly proposes three kinds of rules based on formal concepts and dual concepts. In addition, the methods of rule acquisition for different kinds of rules are presented. Finally, the attribute reduction approaches to preserve different kinds of rules are given by using discernibility matrix.

Supported by the National Natural Science Foundation of China (Grant No. 61976130).

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

References

  1. Chen, Y., Yao, Y.: A multiview approach for intelligent data analysis based on data operators. Inf. Sci. 178(1), 1–20 (2008)

    Article  MathSciNet  Google Scholar 

  2. Dntsch, I., Gediga, G.: Modal-style operators in qualitative data analysis. In: IEEE International Conference on Data Mining (2002)

    Google Scholar 

  3. Ganter, B., Stumme, G., Wille, R.: Formal Concept Analysis: Foundations and Applications (2005)

    Google Scholar 

  4. Ganter, B., Wille, R.: Formal Concept Analysis (1999)

    Google Scholar 

  5. Godin, R., Missaoui, R., Alaoui, H.: Incremental concept formation algorithms based on galois (concept) lattices. Comput. Intell. 11(2), 246–267 (2010)

    Article  Google Scholar 

  6. Hao, F., Pang, G., Pei, Z., Qin, K.Y., Zhang, Y., Wang, X.: Virtual machines scheduling in mobile edge computing: a formal concept analysis approach. IEEE Trans. Sustain. Comput. (2019)

    Google Scholar 

  7. Hao, F., Park, D.S., Min, G., Jeong, Y.S., Park, J.H.: k-cliques mining in dynamic social networks based on triadic formal concept analysis. Neurocomputing 209, 57–66 (2016)

    Article  Google Scholar 

  8. Li, J., Mei, C., Lv, Y.: A heuristic knowledge-reduction method for decision formal contexts. Comput. Math. Appl. 61(4), 1096–1106 (2011)

    Article  MathSciNet  Google Scholar 

  9. Li, J., Mei, C., Lv, Y.: Knowledge reduction in decision formal contexts. Knowl.-Based Syst. 24(5), 709–715 (2011)

    Article  Google Scholar 

  10. Li, J., Mei, C., Wang, J., Zhang, X.: Rule-preserved object compression in formal decision contexts using concept lattices. Knowl.-Based Syst. 71, 435–445 (2014)

    Article  Google Scholar 

  11. Liaab, J.: Knowledge reduction in real decision formal contexts. Inf. Sci. 189(7), 191–207 (2012)

    MathSciNet  Google Scholar 

  12. Neuss, C., Kent, R.E.: Conceptual analysis of resource meta-information. Comput. Netw. ISDN Syst. 27(6), 973–984 (1995)

    Article  Google Scholar 

  13. Nguyen, P.H.P., Dan, C.: A basic mathematical framework for conceptual graphs. IEEE Trans. Knowl. Data Eng. 18(2), 261–271 (2006)

    Article  Google Scholar 

  14. Qin, K., Li, B., Pei, Z.: Attribute reduction and rule acquisition of formal decision context based on object (property) oriented concept lattices (2019)

    Google Scholar 

  15. Shao, M., Leung, Y., Wang, X., Wu, W.: Granular reducts of formal fuzzy contexts. Knowl.-Based Syst. 114, 156–166 (2016)

    Article  Google Scholar 

  16. Shao, M., Leung, Y., Wu, W.: Rule acquisition and complexity reduction in formal decision contexts. Int. J. Approx. Reason. 55(1), 259–274 (2014)

    Article  MathSciNet  Google Scholar 

  17. Shao, M., Yang, H., Wu, W.: Knowledge reduction in formal fuzzy contexts. Knowl.-Based Syst. 73, 265–275 (2015)

    Article  Google Scholar 

  18. Sheng, Q., Qian, Q.: Mining materials knowledge with concept lattice algorithm. Mater. Today Commun. 22, 100726 (2020)

    Article  Google Scholar 

  19. Wan, Q., Wei, L.: Approximate concepts acquisition based on formal contexts. Knowl.-Based Syst. 75, 78–86 (2015)

    Article  Google Scholar 

  20. Wei, L.: Attribute reduction theory of concept lattice based on decision formal contexts. Sci. China 51(7), 910–923 (2008)

    MathSciNet  MATH  Google Scholar 

  21. Wei, L., Liu, L., Qin, J., Qian, T.: Rules acquisition of formal decision contexts based on three-way concept lattices. Inf. Ences 516, 529–544 (2020)

    MathSciNet  Google Scholar 

  22. Wille, R.: Restructuring lattice theory: an approach based on hierarchies of concepts. Orderd Sets D Reidel 83, 314–339 (1982)

    MathSciNet  MATH  Google Scholar 

  23. Wu, W., Leung, Y., Mi, J.: Granular computing and knowledge reduction in formal contexts. IEEE Trans. Knowl. Data Eng. 21(10), 1461–1474 (2009)

    Article  Google Scholar 

  24. Yao, Y.: A comparative study of formal concept analysis and rough set theory in data analysis. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds.) RSCTC 2004. LNCS (LNAI), vol. 3066, pp. 59–68. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-25929-9_6

    Chapter  Google Scholar 

  25. Zhang, W., Ma, J., Fan, S.: Variable threshold concept lattices. Inf. Sci. 177(22), 4883–4892 (2007)

    Article  MathSciNet  Google Scholar 

  26. Zhang, W.-X., Wei, L., Qi, J.-J.: Attribute reduction in concept lattice based on discernibility matrix. In: Ślęzak, D., Yao, J.T., Peters, J.F., Ziarko, W., Hu, X. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3642, pp. 157–165. Springer, Heidelberg (2005). https://doi.org/10.1007/11548706_17

    Chapter  Google Scholar 

  27. Zhang, W., Wei, L., Qi, J.: Attribute reduction theory and approach to concept lattice. Sci. China 48(6), 713–726 (2005)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgement

This work has been partially supported by the National Natural Science Foundation of China (Grant No. 61976130).

Author information

Authors and Affiliations

  1. The School of Information Science and Technology, Southwest Jiaotong University, Chengdu, 611756, China

    Qian Hu

  2. School of Mathematics, Southwest Jiaotong University, Chengdu, 611756, China

    Keyun Qin

Authors
  1. Qian Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Keyun Qin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qian Hu .

Editor information

Editors and Affiliations

  1. Ningbo Institute of Industrial Technology, Ningbo, China

    Chaoyi Pang

  2. College of Computer Science, Zhejiang University, Hangzhou, China

    Yunjun Gao

  3. Monash University, Clayton, VIC, Australia

    Guanliang Chen

  4. Faculty of Automation, Computers and Electronics, University of Craiova, Craiova, Romania

    Elvira Popescu

  5. Department of Physics, Zhejiang University, Hangzhou, China

    Lu Chen

  6. School of Computer Science, South China Normal University, Guangzhou, China

    Tianyong Hao

  7. Zhejiang University, Ningbo, China

    Bailing Zhang

  8. Universidad Internacional De La Rioja, Logroño, Spain

    Silvia Margarita Baldiris Navarro

  9. Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong

    Qing Li

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Hu, Q., Qin, K. (2021). Attribute Reduction and Rule Acquisition of Formal Decision Context Based on Dual Concept Lattice. In: Pang, C., et al. Learning Technologies and Systems. SETE ICWL 2020 2020. Lecture Notes in Computer Science(), vol 12511. Springer, Cham. https://doi.org/10.1007/978-3-030-66906-5_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-66906-5_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-66905-8

  • Online ISBN: 978-3-030-66906-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation