Incomplete Information System and Rough Set Theory: Models by Xibei Yang, Jingyu Yang

By Xibei Yang, Jingyu Yang

"Incomplete details method and tough Set thought: types and characteristic discount rates" covers theoretical research of generalizations of tough set version in numerous incomplete details structures. It discusses not just the standard attributes but in addition the factors within the incomplete details platforms. in line with kinds of tough set versions, the publication offers the sensible ways to compute numerous reducts when it comes to those versions. The e-book is meant for researchers and postgraduate scholars in desktop studying, facts mining and data discovery, particularly if you are operating in tough set conception, and granular computing.

Dr. Xibei Yang is a lecturer on the tuition of laptop technology and Engineering, Jiangsu college of technology and expertise, China; Jingyu Yang is a professor on the institution of desktop technology, Nanjing college of technology and expertise, China.

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Extra resources for Incomplete Information System and Rough Set Theory: Models and Attribute Reductions

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In the second hierarchical structure, if for each equivalence class ([x]R1k ) in the multigranulation space MS R1 , there must be an equivalence class ([x]R2 j ) in the multigranulation space MS R2 such that [x]R1k ⊆ [x]R2 j , then the multigranulation space MS R1 is considered as to be finer than the multigranulation space MS R2 . 20 are two different hierarchical structures, which are proposed on the partitions based multigranulation spaces. The following example will show that there is not a necessary causality between these two hierarchical structures.

Granulation space. Obviously, these information granules are all equivalence classes in the above rough approximations. If we use each subset of attributes in multigranulation rough sets to construct an information system, then it is interesting to explore the relationships between rough sets in multiple-source information systems and multigranulation rough sets. 29. 60) i=1 Proof. We only prove Eq. 57), others can be proved analogously. t. t. 29 shows that the multigranulation rough set can be considered as rough sets in multiple-source information systems and verse vice.

Comput. Biol. Med. : Automated extraction of medical expert system rules from clinical databases based on rough set theory. Inform. Sci. : Automated discovery of positive and negative knowledge in clinical databases. IEEE Eng. Med. Biol. 19, 56–62 (2000) [26] Zhang, Z. , Gao, G. : A rough set-based multiple criteria linear programming approach for the medical diagnosis and prognosis. Expert Syst. Appl. 36, 8932–8937 (2009) [27] Ananthanarayana, V. , Narasimha, M. , Subramanian, D. : Tree structure for efficient data mining using rough sets.

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