Abstract
Rough sets and fuzzy rough sets serve as important approaches to granular computing, but the granular structure of fuzzy rough sets is not as clear as that of classical rough sets since lower and upper approximations in fuzzy rough sets are defined in terms of membership functions, while lower and upper approximations in classical rough sets are defined in terms of union of some basic granules. This limits further investigation of the existing fuzzy rough sets. To bring to light the innate granular structure of fuzzy rough sets, we develop a theory of granular computing based on fuzzy relations in this paper. We propose the concept of granular fuzzy sets based on fuzzy similarity relations, investigate the properties of the proposed granular fuzzy sets using constructive and axiomatic approaches, and study the relationship between granular fuzzy sets and fuzzy relations. We then use the granular fuzzy sets to describe the granular structures of lower and upper approximations of a fuzzy set within the framework of granular computing. Finally, we characterize the structure of attribute reduction in terms of granular fuzzy sets, and two examples are also employed to illustrate our idea in this paper.
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References
Bargiela A, Pedrycz W (2003) Granular computing: an introduction. Kluwer, Boston
Bhatt R, Gopal M (2005) On fuzzy rough sets approach to feature selection. Pattern Recogn Lett 26(7):965–975
Castellano G, Fanelli A (2001) Information granulation via neural network-based learning. In: IFSA world Congress and 20th NAFIPS international conference, vol 5, pp 3059–3064
Chen D, Tsang E, Zhao S (2007a) Attribute reduction with \( T_{L} \) fuzzy rough sets. IEEE Int Conf Syst Man Cybernet 1:486–491
Chen D, Wang X, Zhao S (2007b) Attribute reduction based on fuzzy rough sets. RSEISP LNAI 4585:381–390
Deng T, Chen Y, Xu W, Dai H (2007) A novel approach to fuzzy rough sets based on a fuzzy covering. Inf Sci 177(11):2308–2326
Dick S, Schenker A, Pedrycz W, Kandel A (2007) Regranulation: a granular algorithm enabling communication between granular worlds. Inf Sci 177:408–435
Dubois D, Prade H (1990) Rough fuzzy sets and fuzzy rough sets. Int J Gen Syst 17(2–3):191–209
Dubois D, Prade H (1992) Putting rough sets and fuzzy sets together. In: Slowinski R (ed) Intelligent decision support. Handbook of applications and advances of the rough sets theory. Kluwer, Dordrecht
Fernandez S, Murakami S (2003) Rough set analysis of a general type of fuzzy data using transitive aggregations of fuzzy similarity relations. Fuzzy Sets Syst 139:635–660
Hoppner F, Klawonn F, Kruse R, Rankler T (1999) Fuzzy cluster analysis: methods for classification, data analysis, and image recognition. Wiley, New York
Hu Q, Yu D, Xie Z (2006) Information-preserving hybrid data reduction based on fuzzy-rough techniques. Pattern Recogn Lett 27(5):414–423
Jensen R, Shen Q (2004) Fuzzy-rough attributes reduction with application to web categorization. Fuzzy Sets Syst 141:469–485
Li T, Zhang W (2008) Rough fuzzy approximations on two universes of discourse. Inf Sci 178(3):892–906
Liu G (2007) Generalized rough sets over fuzzy lattices. Inf Sci 178(6):1651–1662
Liu Y, Luo M (1997) Fuzzy topology. World Scientific, Singapore
Mi J, Zhang W (2004) An axiomatic characterization of a fuzzy generalization of rough sets. Inf Sci 160(1–4):235–249
Mi J, Leung Y, Zhao H, Feng T (2008) Generalized fuzzy rough sets determined by a triangular norm. Inf Sci 178(16):3203–3213
Mordeson J, Bhutani K, Rosenfeld A (2005) Fuzzy group theory. Springer-Verlag, Berlin
Morsi N, Yakout M (1998) Axiomatics for fuzzy rough sets. Fuzzy Sets Syst 100:327–342
Pawlak Z (1982) Rough sSets. Int J Comput Inform Sci 11(5):341–356
Radzikowska A, Kerre E (2002) A comparative study of fuzzy rough sets. Fuzzy Sets Syst 126:137–155
Serrano-Gotarredona T, Linares-Barranco B, Andreou A (1998) Adaptive resonance theory microchips-circuit design techniques. Kluwer, Boston
Skowron A, Polkowski L (1998) Rough sets in knowledge discovery, vols 1, 2. Springer, Berlin
Skowron A, Rauszer C (1992) The discernibility matrices and functions in information systems. In: Intelligent decision support: handbook of applications and advances of the rough sets theory. Kluwer, Dordrecht, pp 331–362
Slowinski R (1992) Intelligent decision support: handbook of applications and advances of the rough sets theory. Kluwer, Boston
Wu W, Zhang W (2004) Constructive and axiomatic approaches of fuzzy approximation operators. Inf Sci 159(3–4):233–254
Wu W, Mi J, Zhang W (2004) Generalized fuzzy rough sets. Inf Sci 151:263–282
Yang X (2007) Minimization of axiom sets on fuzzy approximation operators. Inf Sci 177(18):3840–3854
Yueng D, Chen D, Tsang E, John W, Wang X (2005) On the generalization of fuzzy rough systems. IEEE Trans Fuzzy Syst 13(3):343–361
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This paper is supported by a grant of NSFC (70871036) and a grant of National Basic Research Program of China (2009CB219801-3).
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Degang, C., Yongping, Y. & Hui, W. Granular computing based on fuzzy similarity relations. Soft Comput 15, 1161–1172 (2011). https://doi.org/10.1007/s00500-010-0583-1
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DOI: https://doi.org/10.1007/s00500-010-0583-1