2010•Unpublished venueRequires access
Applied granular matrix to attribute reduction algorithm
Luo Zhong, Guo Cui-cui, Lei Mei, Lei Hu, Pan Jia-wei, Su Yong-chang
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Abstract
Attribute reduction is an important research area of rough set theory. Based on rough set theory, this paper established the granular matrix with the idea of granular computing, proposed and defined the AND operation of granular computing, established the knowledge granulation method based on granular matrix, and puts forward the attribute reduction algorithm based on granular matrix. The attribute reduction, using granular matrix to select the minimal attribute set, is different from the traditional attribute reduction which acquires the attribute core at first and then selects the best attribute set. Theoretical analysis shows that the new algorithm is reliable and valid. The new algorithm could provide a new paradigm for the attribute reduction of granular computing and a feasible method for further research on granular computing.