Skip to main content
SpringerLink
Log in

Linguistic-valued layered concept lattice and its rule extraction

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

Formal concept analysis as an effective tool for data analysis and knowledge acquisition can be used to describe the potential relation between objects and attributes. In order to handle linguistic uncertainty information with comparability and incomparability, we propose a kind of linguistic-valued formal concept analysis approach based on lattice implication algebra. Firstly, by setting different linguistic-valued trust degrees, we put forward a linguistic-valued layered concept lattice for meeting the requirements of different experts at different levels. Secondly, the rule extraction algorithm of the linguistic-valued layered concept lattice with the trust degree is given to acquire non-redundant linguistic-valued rules with different trust degrees by using the linguistic-valued weakly consistent formal decision context. Then, aiming at the same premise or conclusion for the different rules, we adopt the deleting or uniting strategy to deal with the redundant rules. The updated and simplified rules can make the rule acquisition easier and the linguistic-valued decision rules extracted are more compact. Finally, the effectiveness and practicability of the proposed approach are illustrated by the comparison analysis.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Baixeries J, Kaytoue M, Napoli A (2014) Characterizing functional dependencies in formal concept analysis with pattern structures. Ann Math Artif Intell 72(2):129–149

    Article  MathSciNet  Google Scholar 

  2. Belohlavek R, Vychodil V (2011) What is a fuzzy concept lattice? International workshop on rough sets, fuzzy sets, data mining, and granular-soft computing. Springer, Berlin

    Google Scholar 

  3. Cabrerizo FJ, Morente-Molinera JA, Pedrycz W, Taghavi A, Herrera VE (2018) Granulating linguistic information in decision making under consensus and consistency. Expert Syst Appl 99:83–92

    Article  Google Scholar 

  4. Chen JK, Mi JS, Xie B, Lin YJ (2019) A fast attribute reduction method for large formal decision contexts. Inter J Appro Reason 106:1–17

    Article  MathSciNet  Google Scholar 

  5. Cintra ME, Camargo HA, Monard MC (2016) Genetic generation of fuzzy systems with rule extraction using formal concept analysis. Inf Sci 349–350:199–215

    Article  Google Scholar 

  6. Guimaraes DFAM, Cintra ME, Felix AC, Cavalcante DL (2018) Definition of strategies for crime prevention and combat using fuzzy clustering and formal concept analysis. Int J Uncertain Fuzz Knowl Based Syst 26(03):429–452

    Article  Google Scholar 

  7. Kaytoue M, Kuznetsov SO, Napoli A et al (2011) Mining gene expression data with pattern structures in formal concept analysis. Inf Sci 181(10):1989–2001

    Article  MathSciNet  Google Scholar 

  8. Liu HC, Lin QL, Wu J (2014) Dependent interval 2-tuple linguistic aggregation operators and their application to multiple attribute group decision making. Int J Uncertain Fuzz Knowl Based Syst 22(05):717–735

    Article  MathSciNet  Google Scholar 

  9. Li JH, Huang CC, Mei CL, Yin YQ (2017) An intensive study on rule acquisition in formal decision contexts based on minimal closed label concept lattices. Intell Autom Soft Comput 23:519–533

    Article  Google Scholar 

  10. Li JH, Kumar CA, Mei C, Wang XZ (2017) Comparison of reduction in formal decision contexts. Int J Approx Reason 80:100–122

    Article  MathSciNet  Google Scholar 

  11. Li JH, Mei CL, Lv YJ (2013) Incomplete decision contexts: approximate concept construction, rule acquisition and knowledge reduction. Int J Approx Reason 54(1):149–165

    Article  MathSciNet  Google Scholar 

  12. Li JH, Ren Y, Mei CL, Qian YH, Yang XB (2016) A comparative study of multigranulation rough sets and concept lattices via rule acquisition. Knowl Based Syst 91:152–164

    Article  Google Scholar 

  13. Li JH, Wang JH, Mei CL, Zhang X (2014) Weakly closed label concept lattice and its application to rule acquisition in decision formal contexts. International Conference on Machine Learning & Cybernetics. IEEE, pp 658–663

  14. Liang JY, Wang JH (2006) A New lattice structure and method for extracting association rules based on concept lattice. Int J Comput Sci Net Secur 6(11):107–114

    Google Scholar 

  15. Li JY, Wang X, Wu WZ, Xu YH (2017) Attribute reduction in inconsistent formal decision contexts based on congruence relations. Int J Mach Learn Cybern 8:81–94

    Article  Google Scholar 

  16. Li KW, Shao MW, Wu WZ (2017) A data reduction method in formal fuzzy contexts. Int J Mach Learn Cybern 8(4):1145–1155

    Article  Google Scholar 

  17. Liu PS, Cui H, Cao YM, Hou XH, Zou L (2019) A method of multimedia teaching evaluation based on fuzzy linguistic concept lattice. Multi Tools Appl 78(21):30975–31001

    Article  Google Scholar 

  18. Li WJ, Liu J, Wang H, Alberto C, Rosa MR, Luis M (2012) A qualitative decision making model based on belief linguistic rule based inference methodology. Int J Uncertain Fuzz Knowl Based Syst 20(1):105–118

    Article  Google Scholar 

  19. Liu X, Wang Y, Li XN, Zou L (2017) A linguistic-valued approximate reasoning approach for financial decision making. Int J Comput Intell Syst 10:312–317

    Article  Google Scholar 

  20. Meng D, Pei Z (2013) On weighted unbalanced linguistic aggregation operators in group decision making. Inf Sci 223:31–41

    Article  MathSciNet  Google Scholar 

  21. Ouyang YP, Shieh HM, Tzeng GH, Yen L, Chan CC (2011) Combined rough sets with flow graph and formal concept analysis for business aviation decision-making. J Intell Inf Syst 36(3):347–366

    Article  Google Scholar 

  22. Poelmans J, Ignatov DI, Kuznetsov SO, Dedene G (2013) Formal concept analysis in knowledge processing: a survey on applications. Expert Syst Appl 40(16):6538–6560

    Article  Google Scholar 

  23. Qian T, Wei L, Qi JJ (2017) Decomposition methods of formal contexts to construct concept lattices. Int J Mach Learn Cybern 8:95–108

    Article  Google Scholar 

  24. Qin KY, Li B, Pei Z (2019) Attribute reduction and rule acquisition of formal decision context based on object (property) oriented concept lattices. Int J Mach Learn Cybern 10:2837–2850

    Article  Google Scholar 

  25. Quan TT, Ngo LN, Hui SC (2009) An effective clustering-based approach for conceptual association rules mining. 2009 IEEE-RIVF International Conference on Computing and Communication Technologies. IEEE, pp 1–7

  26. Ruan D (2010) Linguistic values-based intelligent information processing: theory, methods, and applications. Vol. 259. Amsterdam: Atlantis Press

  27. Rodriguez RM, Martinez L, Herrera F (2011) Hesitant fuzzy linguistic term sets for decision making. IEEE Trans Fuzzy Syst 20(1):109–119

    Article  Google Scholar 

  28. Shao MW, Lv MM, Li KW, Wang CZ (2019) The construction of attribute (object)-oriented multi-granularity concept lattices. Int J Mach Learn Cybern 11:1–16

    Google Scholar 

  29. Singh PK (2018) Complex neutrosophic concept lattice and its applications to air quality analysis. Chaos, Solitons Fractals 109:206–213

    Article  Google Scholar 

  30. Singh PK, Kumar CA (2017) Attribute implications in data with fuzzy attributes using armstrong axioms. 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS). IEEE, pp 637–641

  31. Valtchev P, Missaoui R, Godin R (2004) Formal concept analysis for knowledge discovery and data mining: the new challenges. International conference on formal concept analysis. Springer, Berlin, Heidelberg, pp 352–371

  32. Wei L, Li T (2012) Rules acquisition in consistent formal decision contexts. 2012 International Conference on Machine Learning and Cybernetics. Vol. 2. IEEE, pp 801–805

  33. Wei L, Liu L, Qi JJ, Qian T (2020) Rules acquisition of formal decision contexts based on three-way concept lattices. Inf Sci 516:529–544

    Article  MathSciNet  Google Scholar 

  34. Wille R (1982) Restructuring lattice theory: an approach based on hierarchies of concepts. In: Rival I (ed) Ordered sets. Springer, Dordrecht, pp 445–470

    Chapter  Google Scholar 

  35. Wan Y, Zou L (2019) An efficient algorithm for decreasing the granularity levels of attributes in formal concept analysis. IEEE Access 7:11029–11040

  36. Xu WH, Li WT (2016) Granular computing approach to two-way learning based on formal concept analysis in fuzzy datasets. IEEE Trans Cybern 46(2):366–379

    Article  MathSciNet  Google Scholar 

  37. Xu Y, Chen SW, Ma J (2006) Linguistic truth-valued lattice implication algebra and its properties. Proc of the Multiconference on Computational Engineering in Systems Applications. Washington, IEEE, pp 1413–1418

  38. Xu Y, Ruan D, Qin KY, Liu J (2003) Lattice-valued logic: an alternative approach to treat fuzziness and incomparability. Springer-Verlag, Heidelberg

    Book  Google Scholar 

  39. Xu ZS, Wang H (2016) On the syntax and semantics of virtual linguistic terms for information fusion in decision making. Inf Fus 34:43–48

    Article  Google Scholar 

  40. Yang L, Wang Y, Yang X (2008) A method of linguistic truth-valued concept lattice for decision-making. Computational Intelligence in Decision and Control. pp 295–300

  41. Zadeh LA (1999) Fuzzy logic= computing with words. Computing with Words in Information/Intelligent Systems 1. Physica, Heidelberg, pp 3–23

  42. Zadeh LA (1975) The concept of linguistic variable and application to approximate reasoning, Part I, Part II. Part III. Information Sciences 8(4):199–249 (8, 301–357, 9, 43–80)

    Article  Google Scholar 

  43. Zaki MJ (2004) Mining non-redundant association rules. Data Min Knowl Disc 9(3):223–248

    Article  MathSciNet  Google Scholar 

  44. Zhao YX, Li JH, Liu WQ, Xu WH (2017) Cognitive concept learning from incomplete information. Int J Mach Learn Cybern 8:159–170

    Article  Google Scholar 

  45. Zou L, Ruan D, Pei Z, Xu Y (2008) A linguistic truth-valued reasoning approach in decision making with incomparable information. J Intell Fuzzy Syst 19(4):335–343

    MATH  Google Scholar 

  46. Zou L, Wen X, Wang YX (2016) Linguistic truth-valued intuitionistic fuzzy reasoning with applications in human factors engineering. Inf Sci 327:201–216

    Article  MathSciNet  Google Scholar 

  47. Zou L, Zhang YX, Liu X (2016) Linguistic-valued approximate reasoning with Lattice ordered linguistic-valued credibility. Int J Comput Intell Syst 8(1):53–61

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (No.61772250), and Special Foundation for Distinguished Professors of Shandong Jianzhu University.

Author information

Authors and Affiliations

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

    Li Zou

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

    Ning Kang

  3. School of Mathematics, Liaoning Normal University, Dalian, 116029, China

    Lu Che & Xin Liu

Authors
  1. Li Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ning Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lu Che
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xin Liu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zou, L., Kang, N., Che, L. et al. Linguistic-valued layered concept lattice and its rule extraction. Int. J. Mach. Learn. & Cyber. 13, 83–98 (2022). https://doi.org/10.1007/s13042-021-01351-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-021-01351-3

Keywords

Search

Navigation