Skip to main content
SpringerLink
Log in

A Transformation Model for Different Granularity Linguistic Concept Formal Context

  • Conference paper
  • First Online:
Artificial Intelligence Logic and Applications (AILA 2022 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1657))

Included in the following conference series:

  • 259 Accesses

Abstract

Due to certain differences in people's knowledge level and personal preferences, for the description of the same thing phenomenon, different people usually choose different granularity of linguistic term sets to make judgments, so it is necessary to propose a transformation method for linguistic concept formal context with different granularity. In this paper, we firstly define the normalized distance between multi-granularity linguistic formal contexts and linguistic terms for different granularity linguistic concept formal context. Then, the normalized distance from linguistic terms to intermediate linguistic terms is kept constant to realize the transformation for different granularity linguistic concept formal context. Finally, under different threshold constraints, we achieve dynamic transformation of formal contexts of linguistic values by adjusting the thresholds. The transformation method process is reversible and it can avoid information loss.

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 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
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. Wille, R.: Restructuring lattice theory: an approach based on hierarchies of concepts. In: Rival, I. (ed.) Ordered Sets, pp. 445–470. Springer, Dordrecht (1982)

    Chapter  Google Scholar 

  2. Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: a survey on applications. Expert Syst. Appl. An Int. J. 40(16), 6538–6560 (2013)

    Article  Google Scholar 

  3. Kaytoue, M., Kuznetsov, S.O., Napoli, A., Duplessis, S.: Mining gene expression data with pattern structures in formal concept analysis. Inform. Sci. 181(10), 1989–2001 (1989)

    Article  MathSciNet  Google Scholar 

  4. Adriana, M., de Farias, G., Cintra, M.E., Felix, A.C., Cavalcante, D.L.: Definition of strategies for crime prevention and combat using fuzzy clustering and formal concept analysis. Int. J. Unc. Fuzz. Knowl.-Based Syst. 26(03), 429–452 (2018). https://doi.org/10.1142/S0218488518500216

    Article  Google Scholar 

  5. Baixeries, J., Kaytoue, M., Napoli, A.: Characterizing functional dependencies in formal concept analysis with pattern structures. Ann. Math. Artif. Intell. 72(1–2), 129–149 (2014). https://doi.org/10.1007/s10472-014-9400-3

    Article  MathSciNet  MATH  Google Scholar 

  6. Zadeh, L.A., Kacprzyk, J.: Computing with Words in Information/Intelligent Systems, vol. 2. Physica-Verlag, HD (1999)

    Book  MATH  Google Scholar 

  7. Pang, K., Kang, N., Chen, S., Zheng, H.: Collaborative filtering recommendation algorithm based on linguistic concept lattice with fuzzy object. In: 2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), pp. 57–63. IEEE (2019)

    Google Scholar 

  8. Zou, L., Pang, K., Song, X., Kang, N., Liu, X.: A knowledge reduction approach for linguistic concept formal context. Inform. Sci. 524, 165–183 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  9. Zou, L., Kang, N., Che, L., Liu, X.: Linguistic-valued layered concept lattice and its rule extraction. Int. J. Mach. Learn. Cybern. 13(1), 83–98 (2021). https://doi.org/10.1007/s13042-021-01351-3

    Article  Google Scholar 

  10. Yao, Y.Y.: Concept lattices in rough set theory. In: IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04, vol. 2, pp. 796–801 (2004)

    Google Scholar 

  11. Wu, W.Z., Chen, Y., Xu, Y.H.: Optimal granularity selections in consistent incomplete multi-granular labeled decision systems. Pattern Recogn. Artif. Intell. 29(2), 108–115 (2016)

    Google Scholar 

  12. Shao, M.-W., Lv, M.-M., Li, K.-W., Wang, C.-Z.: The construction of attribute (object)-oriented multi-granularity concept lattices. Int. J. Mach. Learn. Cybern. 11(5), 1017–1032 (2019). https://doi.org/10.1007/s13042-019-00955-0

    Article  Google Scholar 

  13. Chu, X., et al.: Multi-granularity dominance rough concept attribute reduction over hybrid information systems and its application in clinical decision-making. Inf. Sci. 597, 274–299 (2022)

    Article  Google Scholar 

  14. Hu, Q., Qin, K.Y.: The construction of multi-granularity concept lattices. J. Intell. Fuzzy Syst. 39(3), 2783–2790 (2020)

    Article  Google Scholar 

  15. Liao, H., Zeshui, X., Zeng, X.-J.: Distance and similarity measures for hesitant fuzzy linguistic term sets and their application in multi-criteria decision making. Inform. Sci. 271, 125–142 (2014). https://doi.org/10.1016/j.ins.2014.02.125

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgments

This work is partially supported by the National Natural Science Foundation of P.R. China (No. 61772250, 62176142), Foundation of Liaoning Educational Committee (No. LJ2020007) and Special Foundation for Distinguished Professors of Shandong Jianzhu University.

Author information

Authors and Affiliations

  1. Software Engineering Institute, East China Normal University, Shanghai, 200062, China

    Ning Kang

  2. Information Science and Technology College, Dalian Maritime University, Dalian, 116026, China

    Kuo Pang

  3. School of Computer Science and Technology, Shandong Jianzhu University, Jinan, 250102, China

    Li Zou

  4. School of Computer and Information Technology, Liaoning Normal University, Dalian, 116081, China

    Meiqiao Sun

Authors
  1. Ning Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kuo Pang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Li Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Meiqiao Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kuo Pang .

Editor information

Editors and Affiliations

  1. East China Normal University, Shanghai, China

    Yixiang Chen

  2. Chinese Academy of Sciences, Beijing, China

    Songmao Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kang, N., Pang, K., Zou, L., Sun, M. (2022). A Transformation Model for Different Granularity Linguistic Concept Formal Context. In: Chen, Y., Zhang, S. (eds) Artificial Intelligence Logic and Applications. AILA 2022 2022. Communications in Computer and Information Science, vol 1657. Springer, Singapore. https://doi.org/10.1007/978-981-19-7510-3_12

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-7510-3_12

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-7509-7

  • Online ISBN: 978-981-19-7510-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation