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2007•Journal of Anhui University of TechnologyRequires access

Algorithm for Attribute Reduction Based on Attribute Significance of Binary Discernible Matrix

Xiaoyan Wang

Open publisher page 2 citations

Abstract

Rough set theory is a new method of data mining.Its basic thought is utilizing equivalence relation class,through attribution reduction and rule reduction,to excavate knowledge and reduce knowledge.But the attribution reduction is a NP problem,and it needs to be realized by knowledge of elicitation method.The algorithm for attribute reduction based on attribute significance of binary discernible matrix is proposed.The algorithm can get the smallest attributes quickly and be realized easily.And it is proved to be workable in the theory and practice.

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What this paper is about

Rough set theory is a new method of data mining.Its basic thought is utilizing equivalence relation class,through attribution reduction and rule reduction,to excavate knowledge and reduce knowledge.But the attribution reduction is a NP problem,and it needs to be realized by knowledge of elicitation method.The algorithm for attribute reduction based on attribute significance of binary discernible matrix is proposed.The algorithm can get the smallest attributes quickly and be realized easily.And it is proved to be workable in the theory and practice.

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Available abstract

Rough set theory is a new method of data mining.Its basic thought is utilizing equivalence relation class,through attribution reduction and rule reduction,to excavate knowledge and reduce knowledge.But the attribution reduction is a NP problem,and it needs to be realized by knowledge of elicitation method.The algorithm for attribute reduction based on attribute significance of binary discernible matrix is proposed.The algorithm can get the smallest attributes quickly and be realized easily.And it is proved to be workable in the theory and practice.

Key concepts: Rough set, Binary relation, Reduction (mathematics), Logical matrix, Attribute domain, Equivalence relation, Binary number, Data mining

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