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Theoretical Computer Science
Article
License: Elsevier Non-Commercial
Data sources: UnpayWall
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Theoretical Computer Science
Article . 2013
License: Elsevier Non-Commercial
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Theoretical Computer Science
Article . 2013 . Peer-reviewed
License: Elsevier Non-Commercial
Data sources: Crossref
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A matroidal approach to rough set theory

Authors: William Zhu; Fan Min; Jianguo Tang; Kun She;

A matroidal approach to rough set theory

Abstract

Rough set theory has been successfully applied to vague and uncertain data due to its approximation ability. Matroid is a sophisticated mathematical structure to provide a unifying abstract treatment for graph theory, linear algebra, and combinatorial optimization. In this paper, we redefine rough approximation operators through matroidal approaches and build a matroidal structure of rough set theory. First, each block of a partition is converted to a uniform matroid. In this way, a partition is transformed into a family of uniform matroids. Second, these matroids are combined through the direct sum operation to form a new matroid. Therefore the scattered uniform matroids are treated as a whole one. Third, each concept in a universe is transformed to a restriction matroid. The lower and the upper approximations of each concept are established with the matroidal approach. Fourth, for any two concepts in a universe, the relationships between the approximations of them are discussed and some new properties are revealed. These properties can be hardly found without the help of matroid theory. Fifth, the boundary region and the negative region of a concept in the universe are established directly with the matroidal approach. The lower and the upper approximations of each concept are then obtained through its boundary region. This work indicates a new approach for studying rough set theory.

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Theoretical Computer Science, Computer Science(all)

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
42
Top 10%
Top 10%
Top 10%
hybrid