Abstract
In this paper, three kinds of adjoint extent-intent and intent-extent operators between two complete lattices are constructed. Three new types of concept granular computing systems are then formulated via a quadruple including two complete lattices and the corresponding adjoint operators. It is showed that all the concepts in any of the concept granular computing systems form a complete lattice. Furthermore, based on the four types of concept granular computing systems, we present four types of rough set approximation operators in a formal context which can characterize different aspect of knowledge. Some important properties of the proposed approximation operators are also proved.
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References
Belohlavek R, De Baets B, Konecny J (2014) Granularity of attributes in formal concept analysis. Inf Sci 260:149–170
Belohlavek R, De Baets B, Outrata J, Vychodil V (2010) Computing the lattice of all fixpoints of a fuzzy closure operator. IEEE Trans Fuzzy Syst 18(3):546–557
Formica A (2012) Semantic web search based on rough sets and fuzzy formal concept analysis. Knowl Based Syst 26:40–47
Ganter B, Wille R (1999) Formal concept analysis. Mathematic foundations. Springer, Berlin
Gediga G, Duntsch I (2002) Modal-style operators in qualitative data analysis. In: Proceedings of the 2002 IEEE international conference on data mining, pp 155–162
Greco S, Matarazzo B, Słowiński R (2002) Rough sets methodology for sorting problems in presence of multiple attributes and criteria. Eur J Oper Res 138:247–259
Guan LH, Wang GY (2012) Generalized approximations defined by nonequivalence relations. Inf Sci 193:163–179
Kang XP, Li DY, Wang SG, Qu KS (2012) Formal concept analysis based on fuzzy granularity base for different granulations. Fuzzy Sets Syst 203:33–48
Kumar CA, Srinivas S (2010) Concept lattice reduction using fuzzy K-means clustering. Expert Syst Appl 37:2696–2704
Lai HL, Zhang DX (2009) Concept lattices of fuzzy contexts: formal concept analysis vs. rough set theory. Int J Approx Reason 50:695–707
Lei YB, Luo MK (2009) Rough concept lattices and domains. Ann Pure Appl Logic 159:333–340
Li JH, Mei CL, Xu WH, Qian YH (2015) Concept learning via granular computing: a cognitive viewpoint. Inf Sci 298:447–467
Li JH, Ren Y, Mei CL, Qian YH, Yang XB (2015) A comparative study of multigranulation rough sets and concept lattices via rule acquisition. Knowl Based Syst. doi:10.1016/j.knosys.2015.07.024
Liu M, Shao MW, Zhang WX, Wu C (2007) Reduction method for concept lattices based on rough set theory and its application. Computers Math Appl 53(9):1390–1410
Ma JM, Zhang WX, Leung Y, Song XX (2007) Granular computing and dual Galois connection. Inf Sci 177(23):5365–5377
Medina J (2012) Multi-adjoint property-oriented and object-oriented concept lattices. Inf Sci 190:95–106
Medina J, Ojeda-Aciego M (2013) Dual multi-adjoint concept lattices. Inf Sci 225:47–54
Pawlak Z (1982) Rough sets. Int J Computer Inf Sci 11:341–356
Pedrycz W (2014) Allocation of information granularity in optimization and decision-making models: towards building the foundations of granular computing. Eur J Oper Res 232(1):137–145
Pedrycz W, Bargiela A (2002) Granular computing: a granular signature of data. IEEE Trans Syst Man Cybern Part B Cybern 32(2):212–224
Qian YH, Liang JY, Wu WZ, Dang CY (2011) Information granularity in fuzzy binary GrC model. IEEE Trans Fuzzy Syst 19(2):253–264
Qiu GF, Ma JM, Yang HZ, Zhang WX (2010) A mathematical model for concept granular computing systems. Sci China (Information Sciences) 53(7):1397–1408
Shao MW, Liu M, Zhang WX (2007) Set approximations in fuzzy formal concept analysis. Fuzzy Set Syst 158:2627–2640
Shao MW, Leung Y (2014) Relations between granular reduct and dominance reduct in formal contexts. Knowl Based Syst 65:1–11
Shao MW, Leung Y, Wu WZ (2014) Rule acquisition and complexity reduction in formal decision contexts. Int J Approx Reason 55(1):259–274
Wang LD, Liu XD (2008) Concept analysis via rough set and AFS algebra. Inf Sci 178:4125–4137
Wille R (1982) Restructuring lattice theory: an approach based on hierarchies of concepts. In: Rival I (ed) Ordered sets. Reidel, Dordrecht-Boston, pp 445–470
Wu Q, Liu ZT (2009) Real formal concept analysis based on grey-rough set theory. Knowl Based Syst 22:38–45
Wei L, Wan Q (2014) Granular transformation and irreducible element judgment theory based on pictorial diagrams. IEEE Trans Cybern. doi:10.1109/TCYB.2014.2371476
Wu WZ, Mi JS, Zhang WX (2003) Generalized fuzzy rough sets. Inf Sci 151:263–282
Wu WZ, Leung Y (2011) Theory and applications of granular labelled partitions in multi-scale decision tables. Inf Sci 181:3878–3897
Wu WZ, Leung Y, Mi JS (2009) Granular computing and knowledge reduction in formal contexts. IEEE Trans Knowl Data Eng 21(10):1461–1474
Xu WH, Li WT (2014) Granular computing approach to two-way learning based on formal concept analysis in fuzzy datasets. IEEE Trans Cybern. doi:10.1109/TCYB.2014.2361772
Yao JT, Vasilakos AV, Pedrycz W (2013) Granular computing: perspectives and challenges. IEEE Trans Cybern 43(6):1977–1989
Yao YY (1998) Relational interpretations of neighborhood operators and rough set approximation operators. Inf Sci 111(1–4):239–259
Yao YY (2004) Concept lattices in rough set theory. In: Proceedings of 2004 annual meeting of the North American fuzzy information processing Society, pp 73–78
Yao YY (2004) A comparative study of formal concept analysis and rough set theory. In: Data analysis, rough sets and current trends in computing, 4th International Conference. Lecture Notes in Computer Science, vol 3066, pp 59–68
Yao YY (2005) Perspectives of granular computing. In: Proceedings of 2005 IEEE International conference on granular computing, vol 1, pp 85–90
Yao YY (2005) A united framework of granular computing. In: Pedrycz W, Skowron A, Kreinovich V (eds) Handbook of granular computing. Wiley, New York, pp 401–410
Yao YY (2009) Interpreting concept learning in cognitive informatics and granular computing. IEEE Trans Syst Man Cybern Part B Cybern 39(4):855–866
Yao YY, Zhang LQ (2012) A measurement theory view on the granularity of partitions. Inf Sci 213:1–13
Yao YY, Zhang N, Miao DQ, Xu FF (2012) Set-theoretic approaches to granular computing. Fundamenta Informaticae 115(2–3):247–264
Zadeh LA (1979) Fuzzy sets and information granularity. In: Advances in fuzzy set theory and applications. NorthHolland, Amsterdam, The Netherlands, pp 3–18
Zhang QH, Xing YK (2010) Formal concept analysis based on granular computing. J Comput Inf Syst 6(7):2287–2296
Zhang WX, Ma JM, Fan SQ (2007) Variable threshold concept lattices. Inf Sci 177(22):4883–4892
Zhang WX, Yang HZ, Ma JM, Qiu GF (2009) Concept granular computing based on lattice theoretic setting. Stud Comput Intell 182:67–94
Zhang XY, Miao DQ (2014) Quantitative information architecture, granular computing and rough set models in the double-quantitative approximation space of precision and grade. Inf Sci 268:147–168
Zhu W (2007) Generalized rough sets based on relations. Inf Sci 177(22):4997–5011
Acknowledgments
The authors are very indebted to the anonymous referees for their critical comments and suggestions for the improvement of this paper. This work was supported by the grants from the National Natural Science Foundation of China (Nos. 61173181, 61272021, 61363056,6157332), the National Social Science Foundation of China (No. 14XXW004), the Humanities and Social Science funds Project of Ministry of Education of China (Nos. 11XJJAZH001, 12YJA630019), the Fundamental Research Funds for the Central Universities, the Zhejiang Provincial Nature Science Foundation of China (No. LZ12F03002), and Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province (No. OBDMA201504).
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Gong, F., Shao, MW. & Qiu, G. Concept granular computing systems and their approximation operators. Int. J. Mach. Learn. & Cyber. 8, 627–640 (2017). https://doi.org/10.1007/s13042-015-0457-z
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DOI: https://doi.org/10.1007/s13042-015-0457-z