不完备信息系统及粗糙集理论：模型与属性约简（英文版）
出版时间：2012年版
内容简介
《不完备信息系统及粗糙集理论:模型与属性约简(英文版)》主要介绍粗糙集理论及其在不完备信息系统中的拓展。粗糙集理论是用于处理不精确问题的数学理论，是经典集合论的重要发展。由于粗糙集理论建立在等价关系的基础上，因而不能用来处理具有未知属性值的不完备信息系统，因而如何在不完备信息系统中扩展粗糙集的相关概念对于粗糙集的发展具有极其重要的意义。
《不完备信息系统及粗糙集理论:模型与属性约简(英文版)》力图概括国内外最新的研究成果，主要内容有：粗糙集理论的基本概念、具有遗漏型和缺席型未知属性值的不完备信息系统及相关扩展模型，邻域系统及不完备信息系统中基于邻域系统的粗糙集模型与属性约简方法，不完备信息系统中的序关系粗糙集模型及序关系模糊粗糙集模型，集值信息系统和区间值信息系统中的扩展粗糙集模型等。
目录
Part I Indiscernibility Relation Based Rough Sets
Chapter 1 indiscernibility Relation, Rough Sets and Information System
1.1 Pawlak's Rough Approximation
1.1.1 Rough Set
1.1.2 Uncertainty Measurements and Knowledge Granulation
1.1.3 Knowledge Reductions
1.1.4 Knowledge Dependency
1.2 Variable Precision Rough Set
1.2.1 Inclusion Error and Variable Precision Rough Set
1.2.2 Several Reducts in Variable Precision Rough Set
1.3 Multigranulation Rough Set
1.3.1 Optimistic Multigranulation Rough Set
1.3.2 Pessimistic Multigranulation Rough Set
1.3.3 Multigranulation Rough Memberships
1.4 Hierarchical Structures on Multigranulation Spaces
1.4.1 Definitions of Three Hierarchical Structures
1.4.2 Relationships Between Hierarchical Structures and Multigranulation Rough Sets
1.5 Information System
1.5.1 Information System and Rough Set
1.5.2 Rough Sets in Multiple-source Information Systems
1.5.3 Several Reducts in Decision System
1.6 Conclusions
References
Part II Incomplete Information Systems and Rough Sets
Chapter 2 Expansions of Rough Sets in Incomplete Information Systems..
2.1 Tolerance Relation Based Rough Set Approach
2.1.1 Tolerance Relation and Its Reducts
2.1.2 Tolerance Relation Based Rough Set and Generalized Decision Reduct
2.2 Valued Tolerance Relation Based Rough Set Approach
2.2.1 Valued Tolerance Relation
2.2.2 Valued Tolerance Relation Based Fuzzy Rough Set
2.3 Maximal Consistent Block Based Rough Set Approach
2.3.1 Maximal Consistent Block and Its Reducts
2.3.2 Maximal Consistent Block Based Rough Set and Approximate Distribution Reducts
2.4 Descriptor Based Rough Set
2.4.1 Descriptor and Reduct Descriptor
2.4.2 Descriptor Based Rough Set and Generalized Decision Reduct of Descriptor
2.5 Similarity Relation Based Rough Set Approach
2.5.1 Similarity Relation and Similarity Based Rough Set
2.5.2 Approximate Distribution Reducts in Similarity Relation Based Rough Set
2.6 Difference Relation Based Rough Set Approach
2.6.1 Difference Relation and Its Reducts
2.6.2 Rough Set Based on Difference Relation
2.6.3 Approximate Distribution Reducts in Difference Relation Based Rough Set
2.7 Limited Tolerance Relation Based Rough Set Approach
2.7.1 Limited Tolerance Relation
2.7.2 Limited Tolerance Relation Based Rough Set
2.8 Characteristic Relation Based Rough Set Approach
2.8.1 Characteristic Relation and Characteristic Relation Based Rough Set
2.8.2 Approximate Distribution Reducts in Characteristic Relation Based Rough Set
2.9 Conclusions
References
Chapter 3 Neighborhood System and Rough Set in Incomplete Information System
3.1 Neighborhood System
3.1.1 From Granular Computing to Neighborhood System
3.1.2 Binary Neighborhood System
3.1.3 Covering and Neighborhood System
3.1.4 Fuzzy Neighborhood System
3.1.5 Neighborhood System and Topological Space
3.1.6 Knowledge Operation in Neighborhood System
3.2 Neighborhood System and Rough Approximations
3.2.1 Neighborhood System Based Rough Sets
3.2.2 Relationship Between Neighborhood System Based Rough Set and VPRS
3.2.3 Neighborhood System Based Rough Approximations in Incomplete Information System
3.3 Reducts Neighborhood Systems
3.3.1 Reducts Neighborhood Systems in Incomplete Information System
3.3.2 Neighborhood Systems Based Approximate Distribution Reducts
3.4 Conclusions
References
Part III Dominance-based Rough Sets and Incomplete Information Systems
Chapter 4 Dominance-based Rough Sets in "," Incomplete Information System
Chapter 5 Dominance-based Rough Sets in "？" Incomplete Information System
Part IV Incomplete Information Systems and Multigranulation Rough Sets
Chapter 6 Multigranulation Rough Sets in Incomplete Information System
