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2012•Journal of Changchun University of TechnologyRequires access

An attribute reduction algorithm in the incomplete information system based on the attribute significance

Zhen Chen

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Abstract

This paper proposes an attribute reduction algorithm based on attribute significance in the incomplete information system.The algorithm makes use of the concept of similar matrix via tolerance relationship.In the similar matrix,attribute significance reflects the ability of distinguishing between objects.The more frequent the appearance times are,the less importance the attribute is.The attribute reflects the higher similarity of objects.Then a new algorithm is presented which adds the attribute into the reduction set based on the attribute significance.Experiment results show that the algorithm is correct and effective.

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What this paper is about

This paper proposes an attribute reduction algorithm based on attribute significance in the incomplete information system.The algorithm makes use of the concept of similar matrix via tolerance relationship.In the similar matrix,attribute significance reflects the ability of distinguishing between objects.The more frequent the appearance times are,the less importance the attribute is.The attribute reflects the higher similarity of objects.Then a new algorithm is presented which adds the attribute into the reduction set based on the attribute significance.Experiment results show that the algorithm is correct and effective.

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Available abstract

This paper proposes an attribute reduction algorithm based on attribute significance in the incomplete information system.The algorithm makes use of the concept of similar matrix via tolerance relationship.In the similar matrix,attribute significance reflects the ability of distinguishing between objects.The more frequent the appearance times are,the less importance the attribute is.The attribute reflects the higher similarity of objects.Then a new algorithm is presented which adds the attribute into the reduction set based on the attribute significance.Experiment results show that the algorithm is correct and effective.

Key concepts: Attribute domain, Reduction (mathematics), Variable and attribute, Rough set, Similarity (geometry), Data mining, Set (abstract data type), Computer science

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