A mathematical model for concept granular computing systems

Extent-intent and intent-extent operators are introduced between two complete lattices in this paper and a mathematical model for concept granular computing system is established. We proved that the set of all concepts in this system is a lattice with the greatest element and the least element. This framework includes formal concept lattices from formal contexts, L fuzzy concept lattices from L fuzzy formal contexts and three kinds of variable threshold concept lattices, i.e. the extension and the intension of a concept are a crisp set and a crisp set, a crisp set and a fuzzy set, a fuzzy set and a crisp set, respectively. Finally, some iterative algorithms for constructing concepts are proposed and they are proved to be optimal concepts under some conditions in this system.

[1]  R. Wille Concept lattices and conceptual knowledge systems , 1992 .

[2]  Yiyu Yao,et al.  A multiview approach for intelligent data analysis based on data operators , 2008, Inf. Sci..

[3]  Ramón Fuentes-González,et al.  Concept lattices defined from implication operators , 2000, Fuzzy Sets Syst..

[4]  Samir Elloumi,et al.  Galois connection, formal concepts and Galois lattice in real relations: application in a real classifier , 2002, J. Syst. Softw..

[5]  Vijay K. Rohatgi,et al.  Advances in Fuzzy Set Theory and Applications , 1980 .

[6]  Lotfi A. Zadeh,et al.  Fuzzy sets and information granularity , 1996 .

[7]  Wei Xu,et al.  Fuzzy inference based on fuzzy concept lattice , 2006, Fuzzy Sets Syst..

[8]  Wen-Xiu Zhang,et al.  Attribute reduction theory and approach to concept lattice , 2007, Science in China Series F: Information Sciences.

[9]  Yiyu Yao,et al.  Concept lattices in rough set theory , 2004, IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04..

[10]  Atsuo Murata,et al.  Rough Set Approximations in Formal Concept Analysis , 2010, Trans. Rough Sets.

[11]  Ramón Fuentes-González,et al.  Construction of the L-fuzzy concept lattice , 1998, Fuzzy Sets Syst..

[12]  Wen-Xiu Zhang,et al.  Variable Threshold Concept Lattice and Dependence Space , 2006, FSKD.

[13]  Radim Belohlávek,et al.  A note on variable threshold concept lattices: Threshold-based operators are reducible to classical concept-forming operators , 2007, Inf. Sci..

[14]  Ramón Fuentes-González,et al.  The study of the L-fuzzy concept lattice , 1994 .

[15]  Yuehwern Yih,et al.  Knowledge acquisition through information granulation for imbalanced data , 2006, Expert Syst. Appl..

[16]  Lech Polkowski,et al.  Rough Sets in Knowledge Discovery 2 , 1998 .

[17]  Wen-Xiu Zhang,et al.  Consistency degrees of finite theories in ukasiewicz propositional fuzzy logic , 2005, Fuzzy Sets Syst..

[18]  Bernhard Ganter,et al.  Concept Lattices of Contexts , 1999 .

[19]  Radim Belohlávek,et al.  Concept lattices and order in fuzzy logic , 2004, Ann. Pure Appl. Log..

[20]  Bernhard Ganter,et al.  Formal Concept Analysis: Mathematical Foundations , 1998 .

[21]  Lotfi A. Zadeh,et al.  Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic , 1997, Fuzzy Sets Syst..

[22]  Rudolf Wille,et al.  Restructuring Lattice Theory: An Approach Based on Hierarchies of Concepts , 2009, ICFCA.

[23]  Guofang Qiu,et al.  Learning Models Based on Formal Concept , 2007, RSKT.

[24]  Radim Belohlávek,et al.  Fuzzy Galois Connections , 1999, Math. Log. Q..

[25]  Zhang Wen-xiu,et al.  Attribute reduction theory and approach to concept lattice , 2005 .

[26]  Robert E. Kent,et al.  Rough Concept Analysis: A Synthesis of Rough Sets and Formal Concept Analysis , 1996, Fundam. Informaticae.

[27]  Yee Leung,et al.  Granular computing and dual Galois connection , 2007, Inf. Sci..

[28]  Witold Pedrycz,et al.  Regranulation: A granular algorithm enabling communication between granular worlds , 2007, Inf. Sci..

[29]  Y. Yao Information granulation and rough set approximation , 2001 .