A rough set based dynamic maintenance approach for approximations in coarsening and refining attribute values
ABSTRACT In rough set theory, upper and lower approximations for a concept will change dynamically as the information system changes over time. How to update approximations based on the original information is an important task that can help improve the efficiency of knowledge discovery. This paper focuses on the approach of dynamically updating approximations when attribute values are coarsened or refined. The main contributions include: (1) defining coarsening and refining attribute values in information systems and introducing the properties and the principles of coarsening and refining attribute values; (2) analyzing the properties for dynamic maintenance in terms of upper and lower approximations with coarsening and refining attribute values; (3) proposing an incremental algorithm for updating the approximations of a concept as coarsening or refining attributes values; and finally (4) validating the efficiency of the proposed approach to handle the dynamic maintenance of the approximations for a given concept. © 2010 Wiley Periodicals, Inc.
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ABSTRACT: Set-valued information systems are generalized models of single-valued information sys-tems. 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 ma-trices of H(X), denoted by H [μ,ν] (X), H (μ,ν] (X), H [μ,ν) (X) and H (μ,ν) (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 experi-ments 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.International Journal of Approximate Reasoning 06/2012; 53(4):620-635. DOI:10.1016/j.ijar.2012.01.001 · 1.98 Impact Factor
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ABSTRACT: As business information quickly varies with time, the extraction of knowledge from the related dynamically changing database is vital for business decision making. For an incremental learning optimization on knowledge discovery, a new incremental matrix describes the changes of the system. An optimization incremental algorithm induces interesting knowledge when the object set varies over time. Experimental results validate the feasibility of the incremental learning optimization.
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ABSTRACT: Approximations of a concept by a variable precision rough-set model (VPRS) usually vary under a dynamic information system environment. It is thus effective to carry out incremental updating approximations by utilizing previous data structures. This paper focuses on a new incremental method for updating approximations of VPRS while objects in the information system dynamically alter. It discusses properties of information granulation and approximations under the dynamic environment while objects in the universe evolve over time. The variation of an attribute's domain is also considered to perform incremental updating for approximations under VPRS. Finally, an extensive experimental evaluation validates the efficiency of the proposed method for dynamic maintenance of VPRS approximations.IEEE Transactions on Knowledge and Data Engineering 01/2011; 25(99-PP):1 - 1. DOI:10.1109/TKDE.2011.220 · 1.82 Impact Factor