Three-way fuzzy concept lattice representation using neutrosophic set

Wille R (1982) Restructuring lattice theory: an approach based on hierarchies of concepts. In: Rival I (ed) Ordered Sets, NATO Advanced Study Institutes Series, vol 83, pp 445–470

Singh PK, Aswani Kumar C, Abdullah G (2016) A comprehensive survey on formal concept analysis, its research trends and applications. Int J Appl Math Comput Sci 26(2):495–516

Ganter B, Wille R (1999) Formal concept analysis: mathematical foundation. Springer, Berlin

Burusco A, Fuentes-Gonzalez R (1994) The study of the L-fuzzy concept lattice. Matheware Soft Comput 1(3):209–218

Antoni L, Krajči S, Kŕidlo O, Macek B, Piskova L (2014) On heterogeneous formal contexts. Fuzzy Sets Syst 234:22–33

Burusco A, Fuentes-Gonzales R (2001) The study on interval-valued contexts. Fuzzy Sets Syst 121(3):439–452

Singh PK, Aswani Kumar C (2014) Bipolar fuzzy graph representation of concept lattice. Inf Sci 288:437–448

Singh PK (2016) Processing linked formal fuzzy context using non-commutative composition. Inst Integr Omics Appl Biotechnol (IIOAB) J 7(5):21–32

Djouadi Y, Prade H (2011) Possibility-theoretic extension of derivation operators in formal concept analysis over fuzzy lattices. Fuzzy Optim Decis Mak 10:287–309

Yao YY (2004) A comparative study of formal concept analysis and rough set theory in data analysis. In: Proceedings of 4th International Conference on Rough Sets and Current Trends in Computing (RSCTC) 2004, Sweden, pp 59–68

Qi J, Wei L, Yao Y (2014) Three-way formal concept analysis. Lect Notes Comput Sci 8818:732–741

Qi J, Qian T, Wei L (2016) The connections between three-way and classical concept lattices. Knowl Based Syst 91:143–151

Ma L, Mi JS, Xie B (2016) Multi-scaled concept lattices based on neighborhood systems. Int J Mach Learn Cybern. doi: 10.1007/s13042-016-0521-3

Tang Y, Fan M, Jinhai Li (2016) An information fusion technology for triadic decision contexts. Int J Mach Learn Cybern 7(1):13–24

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

Li M, Wang J (2016) Approximate concept construction with three-way decisions and attribute reduction in incomplete contexts. Knowl Based Syst 91:165–178

Li J, Huang C, Qi J, Qian Y, Liu W (2016) Three-way cognitive concept learning via multi-granularity. Inf Sci. doi: 10.1016/j.ins.2016.04.051

Li JH, Mei CL, Xu WH, Qian YH (2015) Concept learning via granular computing: a cognitive viewpoint. Inf Sci 298:447–467

Yao YY (2010) Three-way decisions with probabilistic rough sets. Inf Sci 180:341–353

Deng X, Yao Y (2014) Decision-theoretic three-way approximations of fuzzy sets. Inf Sci 279:702–715

Hu BQ (2016) Three-way decision spaces based on partially ordered sets and three-way decisions based on hesitant fuzzy sets. Knowl Based Syst 91:16–31

Shao MW, Yang HZ, Wu WZ (2015) Knowledge reduction in formal fuzzy contexts. Knowl Based Syst 73:265–275

Singh PK, Aswani Kumar C, Jinhai L (2016) Knowledge representation using interval-valued fuzzy concept lattice. Soft Comput 20(4):1485–1502

Pandey LK, Ojha KK, Singh PK, Singh CS, Dwivedi S, Bergey EA (2016) Diatoms image database of India (DIDI): a research tool. Environ Technol Innov 5:148–160

Ashbacher C (2002) Introduction to Neutrosophic Logic. American Research Press, Rehoboth

Smarandache F (1999) A unifying field in logics neutrosophy: neutrosophic probability, set and logic. American Research Press, Rehoboth

Ali M, Smarandache F (2016) Complex neutrosophic set. Neural Comput Appl. doi: 10.1007/s00521-015-2154-y

Pedrycz W (1998) Shadowed sets: representing and processing fuzzy sets. IEEE Trans Syst Man Cybern Part B Cybern 28:103–109

Rivieccio U (2008) Neutrosophic logics: prospects and problems. Fuzzy Sets Syst 159(14):1860–1868

Broumi S, Talea M, Bakali A, Smarandache F (2016) On bipolar single valued neutrosophic graphs. J New Theory 11:84–102

Pramanik S, Biswas P, Giri BC (2016) Hybrid vector similarity measures and their applications to multi-attribute decision making under neutrosophic environment. Neural Comput Appl. doi: 10.1007/s00521-015-2125-3

Ye J (2013) Multicriteria decision-making method using the correlation coefficient under single-valued neutrosophic environment. Int J Gen Syst 42(4):386–394

Wang XZ, Ashfaq RAR, Fu AM (2015) Fuzziness based sample categorization for classifier performance improvement. J Intell Fuzzy Syst 29(3):1185–1196

Wang XZ, Dong CR (2009) Improving generalization of fuzzy if then rules by maximizing fuzzy entropy. IEEE Trans Fuzzy Syst 17(3):556–567

Wang XZ, Dong LC, Yan JH (2012) Maximum ambiguity based sample selection in fuzzy decision tree induction. IEEE Trans Knowl Data Eng 24(8):1491–1505

Wang XZ, Xing HJ, Li Y, Hua Q, Dong CR (2014) A study on relationship between generalization abilities and fuzziness of base classifiers in ensemble learning. IEEE Trans Fuzzy Syst 23:1638–1654

Singh PK, Aswani Kumar C (2014) A note on bipolar fuzzy graph representation of concept lattice. Int J Comput Sci Math 5(4):381–393

Bělohlávek R, Sklenǎŕ V, Zackpal J (2005) Crisply generated fuzzy concepts. In: Proceedings of ICFCA 2005, LNAI 3403, pp 269–284

Pollandt S (1998) Fuzzy Begriffe. Springer, Berlin

Djouadi Y (2011) Extended Galois derivation operators for information retrieval based on fuzzy formal concept lattice. In: Benferhal S, Goant J (eds) SUM 2011, Springer, LNAI 6929, pp 346–358

Berry A, Sigayret A (2004) Representing concept lattice by a graph. Discrete Appl Math 144(1–2):27–42

Singh PK, Abdullah G (2015) Fuzzy concept lattice reduction using Shannon entropy and Huffman coding. J Appl Nonclassical Logics 25(2):101–119

Singh PL, Aswani Kumar C, Jinhai L (2016) Concepts reduction in formal concept analysis with fuzzy setting using Shannon entropy. Int J Mach Learn Cybern. doi: 10.1007/s13042-014-0313-6