On the structure of the multigranulation rough set model

The original rough set model, i.e., Pawlak's single-granulation rough set model has been extended to a multigranulation rough set model, where two kinds of multigranulation approximations, i.e., the optimistic and pessimistic approximations were introduced. In this paper, we consider some fundamental properties of the multigranulation rough set model, and show that (i)Both the collection of lower definable sets and that of upper definable sets in the optimistic multigranulation rough set model can form a lattice, such lattices are not distributive, not complemented and pseudo-complemented in the general case. The collection of definable sets in the optimistic multigranulation rough set model does not even form a lattice in general conditions. (ii)The collection of (lower, upper) definable sets in the optimistic multigranulation rough set model forms a topology on the universe if and only the optimistic multigranulation rough set model is equivalent to Pawlak's single-granulation rough set model. (iii)In the context of the pessimistic multigranulation rough set model, the collections of three different kinds of definable sets coincide with each other, and they determine a clopen topology on the universe, furthermore, they form a Boolean algebra under the usual set-theoretic operations.

[1]  Witold Pedrycz,et al.  Positive approximation: An accelerator for attribute reduction in rough set theory , 2010, Artif. Intell..

[2]  William Zhu,et al.  The algebraic structures of generalized rough set theory , 2008, Inf. Sci..

[3]  Jiye Liang,et al.  Incomplete Multigranulation Rough Set , 2010, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[4]  Michiro Kondo,et al.  On the structure of generalized rough sets , 2006, Inf. Sci..

[5]  William Zhu,et al.  Generalized rough sets based on relations , 2007, Inf. Sci..

[6]  H. M. Abu-Donia,et al.  Comparison between different kinds of approximations by using a family of binary relations , 2008, Knowl. Based Syst..

[7]  Yee Leung,et al.  Theory and applications of granular labelled partitions in multi-scale decision tables , 2011, Inf. Sci..

[8]  Md. Aquil Khan,et al.  Formal reasoning with rough sets in multiple-source approximation systems , 2008, Int. J. Approx. Reason..

[9]  Ping Zhu,et al.  Covering rough sets based on neighborhoods: An approach without using neighborhoods , 2009, Int. J. Approx. Reason..

[10]  T. Iwiński Algebraic approach to rough sets , 1987 .

[11]  Gianpiero Cattaneo,et al.  An Investigation About Rough Set Theory: Some Foundational and Mathematical Aspects , 2011, Fundam. Informaticae.

[12]  H. M. Abu-Donia,et al.  Multi knowledge based rough approximations and applications , 2012, Knowl. Based Syst..

[13]  Zheng Pei,et al.  On the topological properties of fuzzy rough sets , 2005, Fuzzy Sets Syst..

[14]  D. Dubois,et al.  ROUGH FUZZY SETS AND FUZZY ROUGH SETS , 1990 .

[15]  Yiyu Yao,et al.  Relational Interpretations of Neigborhood Operators and Rough Set Approximation Operators , 1998, Inf. Sci..

[16]  Daniel Vanderpooten,et al.  A Generalized Definition of Rough Approximations Based on Similarity , 2000, IEEE Trans. Knowl. Data Eng..

[17]  Sheng-Gang Li,et al.  Transformation of bipolar fuzzy rough set models , 2012, Knowl. Based Syst..

[18]  Lingyun Yang,et al.  Topological properties of generalized approximation spaces , 2011, Inf. Sci..

[19]  Yiyu Yao,et al.  Two views of the theory of rough sets in finite universes , 1996, Int. J. Approx. Reason..

[20]  Mihir K. Chakraborty,et al.  A Geometry of Approximation: Rough Set Theory: Logic, Algebra and Topology of Conceptual Patterns , 2008 .

[21]  Zongben Xu,et al.  Transformation of rough set models , 2007, Knowl. Based Syst..

[22]  Yiyu Yao,et al.  MGRS: A multi-granulation rough set , 2010, Inf. Sci..

[23]  Wen-Xiu Zhang,et al.  An axiomatic characterization of a fuzzy generalization of rough sets , 2004, Inf. Sci..

[24]  Andrzej Skowron,et al.  Tolerance Approximation Spaces , 1996, Fundam. Informaticae.

[25]  Fei-Yue Wang,et al.  Reduction and axiomization of covering generalized rough sets , 2003, Inf. Sci..

[26]  Daowu Pei,et al.  A generalized model of fuzzy rough sets , 2005, Int. J. Gen. Syst..

[27]  Liping An,et al.  Rough approximations based on intersection of indiscernibility, similarity and outranking relations , 2010, Knowl. Based Syst..

[28]  Ming Zhang,et al.  Neighborhood systems-based rough sets in incomplete information system , 2011, Knowl. Based Syst..

[29]  Degang Chen,et al.  Fuzzy rough set based attribute reduction for information systems with fuzzy decisions , 2011, Knowl. Based Syst..

[30]  Wei-Zhi Wu,et al.  Generalized fuzzy rough sets , 2003, Inf. Sci..

[31]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.

[32]  Guilong Liu,et al.  Rough set theory based on two universal sets and its applications , 2010, Knowl. Based Syst..

[33]  Yiyu Yao,et al.  Constructive and Algebraic Methods of the Theory of Rough Sets , 1998, Inf. Sci..

[34]  William Zhu,et al.  Topological approaches to covering rough sets , 2007, Inf. Sci..

[35]  Hai-Long Yang A note on "Rough set theory based on two universal sets and its applications" Knowledge-Based Systems 23 (2010) 110-115 , 2011, Knowl. Based Syst..

[36]  Brian A. Davey,et al.  An Introduction to Lattices and Order , 1989 .

[37]  Jiye Liang,et al.  Knowledge structure, knowledge granulation and knowledge distance in a knowledge base , 2009, Int. J. Approx. Reason..

[38]  Z. Pawlak Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .