Journal
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
Volume 53, Issue 4, Pages 620-635Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ijar.2012.01.001
Keywords
Rough sets; Knowledge discovery; Matrix; Set-valued information systems
Categories
Funding
- National Science Foundation of China [60873108, 61175047, 61100117]
- Youth Social Science Foundation of the Chinese Education Commission [11YJC630127]
- Fundamental Research Funds for the Central Universities [SWJTU11ZT08]
- Doctoral Innovation Foundation of Southwest Jiaotong University [2012ZJB]
- Young Software Innovation Foundation of Sichuan Province, China [2011-017]
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Set-valued information systems are generalized models of single-valued information systems. The attribute set in the set-valued information system may evolve over time when new information arrives. Approximations of a concept by rough set theory need updating for knowledge discovery or other related tasks. Based on a matrix representation of rough set approximations, a basic vector H(X) is induced from the relation matrix. Four cut matrices of H(X), denoted by H-[mu,H- nu] (X), H-(mu,H- nu] (X), H-[mu,H- nu) (X) and H-(mu,H- nu) (X), are derived for the approximations, positive, boundary and negative regions intuitively. The variation of the relation matrix is discussed while the system varies over time. The incremental approaches for updating the relation matrix are proposed to update rough set approximations. The algorithms corresponding to the incremental approaches are presented. Extensive experiments on different data sets from UCI and user-defined data sets show that the proposed incremental approaches effectively reduce the computational time in comparison with the non-incremental approach. (C) 2012 Elsevier Inc. All rights reserved.
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