Abstract
As the basic form of data presentation, formal contexts play an elementary and important role in formal concept analysis and in 3-way concept analysis. In fact, many data tables are similar in form to formal contexts. Therefore, these data tables can be studied collectively in a similar framework, and such a study can be significant in knowledge discovery. We propose the notion of 3-valued formal contexts after analyzing the shared characteristics of different data forms such as incomplete formal contexts, conflict situations and other similar cases. After close studies of 3-valued formal contexts, this paper adopts 3-way concept analysis to define 3-valued operators and construct 3-valued concept lattices and finally interpret the meaning of 3-valued operators and discuss the relationship between 3-valued lattices and existing approximation concept lattices. The essence of this method is to present, via 3-way concept analysis, potential information and structure. And 3-way concept analysis shows the common properties of the objects, jointly possessed or jointly not possessed, positive or negative, even the uncertain information. So, this paper actually provides a new model for cognition. Apart from the universal applicability, 3-valued contexts can also be fixed into formal concept analysis. That is, many problems can be studied in the framework of formal concept analysis.






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Wille R. Restructuring lattice theory: an approach based on hierarchies of concepts. In: Rival I, editor. Ordered sets. Dordrecht-Boston: Reidel Publishing Company; 1982. p. 445–70.
Ganter B, Wille R. Formal concept analysis: mathematical foundations. Berlin Heidelberg: Springer-Verlag; 1999.
Kuznetsov SO, Obiedkov SA. Comparing performance of algorithms for generating concept lattices. J Exp Theo Art Intell. 2002;14(2–3):189–216.
Tonella P. Using a concept lattice of decomposition slices for program understanding and impact analysis. IEEE Trans Soft Eng. 2003;29(6):495–509.
Carpineto C, Romano G. Concept data analysis: theory and applications. John Wiley & Sons; 2004.
Qi J, Wei L, Li Z. A partitional view of concept lattice. In: Slezak D, Wang G, Szczuka M, Duntsch I, Yao Y, editors. Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. vol. 3641 of Lecture Notes in Computer Science. Springer Berlin Heidelberg; 2005. p. 74–83.
Zhang W, Wei L, Qi J. Attribute reduction theory and approach to concept lattice. Science In China Series F: Info Sci. 2005;48(6):713–26.
Wu WZ, Leung Y, Mi JS. Granular computing and knowledge reduction in formal contexts. Knowl Data Eng, IEEE Trans. 2009;21(10):1461–74.
Li J, Mei C, Lv Y. Incomplete decision contexts: approximate concept construction, rule acquisition and knowledge reduction. Int J Approx Reas. 2013;54(1):149–65.
Poelmans J, Ignatov DI, Kuznetsov SO, Dedene G. Formal concept analysis in knowledge processing: a survey on applications. Exp Syst Appl. 2013;40(16):6538–60.
Pei Z, Ruan D, Meng D, Liu Z. Formal concept analysis based on the topology for attributes of a formal context. Info Sci. 2013;236:66–82.
Poelmans J, Kuznetsov SO, Ignatov DI, Dedene G. Formal concept analysis in knowledge processing: a survey on models and techniques. Exp Syst Appl. 2013;40(16):6601–23.
Li J, Mei C, Wang J, Zhang X. Rule-preserved object compression in formal decision contexts using concept lattices. Knowl-Based Syst. 2014;71:435–45.
Burmeister P, Holzer R. On the treatment of incomplete knowledge in formal concept analysis. In: Ganter B, Mineau GW, editors. Conceptual Structures: Logical, Linguistic, and Computational Issues. Springer, Berlin Heidelberg: Berlin, Heidelberg; 2000. p. 385–98.
Shao MW, Yang HZ, Wu WZ. Knowledge reduction in formal fuzzy contexts. Knowl-Based Syst. 2015;73:265–75.
Li J, Mei C, Xu W, Qian Y. Concept learning via granular computing: a cognitive viewpoint. Info Sci. 2015;298:447–67.
Wan Q, Wei L. Approximate concepts acquisition based on formal contexts. Knowl-Based Syst. 2015;75:78–86.
Chen X, Qi J, Zhu X, Wang X, Wang Z. Unlabelled text mining methods based on two extension models of concept lattices. Int J Mach Learn Cybernet. 2020;11(2):475–90.
Qi J, Wei L, Yao Y. Three-way formal concept analysis. In: Miao D, Pedrycz W, Slezak D, Peters G, Hu Q, Wang R, editors. Rough Sets and Knowledge Technology. vol. 8818 of Lecture Notes in Computer Science. Springer International Publishing; 2014. p. 732–741.
Yao Y. An outline of a theory of three-way decisions. In: Yao J, Yang Y, Slowinski R, Greco S, Li H, Mitra S, etal., editors. Rough Sets and Current Trends in Computing. vol. 7413 of Lecture Notes in Computer Science. Springer Berlin Heidelberg; 2012. p. 1–17.
Yao Y. Three-way decision: an interpretation of rules in rough set theory. In: Wen P, Li Y, Polkowski L, Yao Y, Tsumoto S, Wang G, editors. Rough Sets and Knowledge Technology. vol. 5589 of Lecture Notes in Computer Science. Springer Berlin Heidelberg; 2009. p. 642–649.
Yao Y. Three-way decisions with probabilistic rough sets. Info Sci. 2010;180(3):341–53.
Yao Y. The superiority of three-way decisions in probabilistic rough set models. Info Sci. 2011;181(6):1080–96.
Li H, Zhou X. Risk decision making based on decision-theoretic rough set: a three-way view decision model. Int J Comput Intell Syst. 2011;4(1):1–11.
Liu D, Li T, Ruan D. Probabilistic model criteria with decision-theoretic rough sets. Info Sci. 2011;181(17):3709–22.
Yang X, Yao J. Modelling multi-agent three-way decisions with decision-theoretic rough sets. Fundamenta Informaticae. 2012;115(2):157–71.
Deng X, Yao Y. Decision-theoretic three-way approximations of fuzzy sets. Info Sci. 2014;279:702–15.
Jia X, Liao W, Tang Z, Shang L. Minimum cost attribute reduction in decision-theoretic rough set models. Info Sci. 2013;219:151–67.
Yao Y. Granular computing and sequential three-way decisions. In: Lingras P, Wolski M, Cornelis C, Mitra S, Wasilewski P, editors. Rough Sets and Knowledge Technology. vol. 8171 of Lecture Notes in Computer Science. Springer Berlin Heidelberg; 2013. p. 16–27.
Yao J, Azam N. Web-based medical decision support systems for three-way medical decision making with game-theoretic rough sets. IEEE Trans Fuzzy Syst. 2015;23(1):3–15.
Yu H, Wang Y, Jiao P. A three-way decisions approach to density-based overlapping clustering. In: Peters JF, Skowron A, Li T, Yang Y, Yao J, Nguyen HS, editors. Transactions on Rough Sets XVIII. vol. 8449 of Lecture Notes in Computer Science. Springer Berlin Heidelberg; 2014. p. 92–109.
Yu H, Zhang C, Hu F. An incremental clustering approach based on three-way decisions. In: Cornelis C, Kryszkiewicz M, Slezak D, Ruiz E, Bello R, Shang L, editors. Rough Sets and Current Trends in Computing. vol. 8536 of Lecture Notes in Computer Science. Springer International Publishing; 2014. p. 152–159.
Zhou B. Multi-class decision-theoretic rough sets. Int J Approx Reas. 2014;55(1):211–24.
Hu BQ. Three-way decisions space and three-way decisions. Info Sci. 2014;281:21–52.
She Y. On determination of thresholds in three-way approximation of many-valued NM-logic. In: Cornelis C, Kryszkiewicz M, Slezak D, Ruiz E, Bello R, Shang L, editors. Rough Sets and Current Trends in Computing. vol. 8536 of Lecture Notes in Computer Science. Springer International Publishing; 2014. p. 136–143.
Yu H, Zhang C, Wang G. A tree-based incremental overlapping clustering method using the three-way decision theory. Knowl-Based Syst. 2016;91:189–203.
Zhang HR, Min F. Three-way recommender systems based on random forests. Knowl-Based Syst. 2016;91:275–86.
Yao Y. Three-way decision and granular computing. Int J Approx Reas. 2018;103:107–23.
Zhang Y, Yao J. Game theoretic approach to shadowed sets: a three-way tradeoff perspective. Info Sci. 2020;507:540–52.
Qi J, Qian T, Wei L. The connections between three-way and classical concept lattices. Knowl-Based Syst. 2016;91(1):143–51.
Wei L, Qian T. The three-way object oriented concept lattice and the three-way property oriented concept lattice. In: 2015 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE; 2015. p. 854–859.
Ren R, Wei L. The attribute reductions of three-way concept lattices. Knowl-Based Syst. 2016;99:92–102.
Qian T, Wei L, Qi J. Constructing three-way concept lattices based on apposition and subposition of formal contexts. Knowl-Based Syst. 2017;116:39–48.
Li J, Huang C, Qi J, Qian Y, Liu W. Three-way cognitive concept learning via multi-granularity. Info Sci. 2017;378(1):244–63.
Huang C, Li J, Mei C, Wu WZ. Three-way concept learning based on cognitive operators: an information fusion viewpoint. Int J Approx Reas. 2017;83:218–42.
Yu H, Li Q, Cai M. Characteristics of three-way concept lattices and three-way rough concept lattices. Knowl-Based Syst. 2018;146:181–9.
Yao Y. Interval sets and three-way concept analysis in incomplete contexts. Int J Mach Learn Cybernet. 2017;8(1):3–20.
Zhi H, Qi J, Qian T, Wei L. Three-way dual concept analysis. Int J Approx Reas. 2019;114:151–65.
Li M, Wang G. Approximate concept construction with three-way decisions and attribute reduction in incomplete contexts. Knowl-Based Syst. 2016;91:165–78.
Pawlak Z. An inquiry into anatomy of conflicts. Info Sci. 1998;109(1):65–78.
Pedrycz W. Shadowed sets: representing and processing fuzzy sets. IEEE Trans Sys, Man, Cybernet, Part B: Cybernet. 1998;28(1):103–9.
Pedrycz W. Granular computing with shadowed sets. In: Slezak D, Wang G, Szczuka M, Duntsch I, Yao Y, editors. Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. Springer, Berlin Heidelberg: Berlin, Heidelberg; 2005. p. 23–32.
Pedrycz W. From fuzzy sets to shadowed sets: interpretation and computing. Int J Intell Syst. 2009;24(1):48–61.
Fan Y, Qi J, Wei L. A conflict analysis model based on three-way decisions. In: Nguyen HS, Ha QT, Li T, Przybyla-Kasperek M, editors. Rough Sets. Cham: Springer International Publishing; 2018. p. 522–32.
Yao Y. Three-way conflict analysis: reformulations and extensions of the Pawlak model. Knowl-Based Syst. 2019;180:26–37.
Lang G, Miao D, Fujita H. Three-way group conflict analysis based on Pythagorean fuzzy set theory. IEEE Trans Fuzzy Syst. 2020;28(3):447–61.
Lipski W. On semantic issues connected with incomplete information databases. ACM Trans Database Syst. 1979;4(3):262–96.
Krupka M, Lastovicka J. Concept lattices of incomplete data. In: Domenach F, Ignatov DI, Poelmans J, editors. Formal Concept Analysis. Springer, Berlin Heidelberg: Berlin, Heidelberg; 2012. p. 180–94.
Djouadi Y, Dubois D, Prade H. Graduality, Uncertainty and Typicality in Formal Concept Analysis. In: Cornelis C, Deschrijver G, Nachtegael M, Schockaert S, Shi Y, editors. 35 Years of Fuzzy Set Theory: Celebratory Volume Dedicated to the Retirement of Etienne E. Kerre. vol. 261 of Studies in Fuzziness and Soft Computing. Berlin, Heidelberg: Springer Berlin Heidelberg; 2010. p. 127–147.
Acknowledgements
This work is partially supported by the National Natural Science Foundation of China (Grant Nos. 61772021, 61976244 and 62006190) and the Natural Science Basic Research Program of Shaanxi (Program No. 2021JM-141). We appreciate Prof. Yiyu Yao for his constructive suggestion to this work.
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Qi, J., Wei, L. & Ren, R. 3-Way Concept Analysis Based on 3-Valued Formal Contexts. Cogn Comput 14, 1900–1912 (2022). https://doi.org/10.1007/s12559-021-09899-6
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DOI: https://doi.org/10.1007/s12559-021-09899-6