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An Attribute Reduct and Attribute Significance Algorithm of Continuous Domain Decide Table

Liu Wenjun

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Abstract

Firstly, the author generalizes the indiscernible relation to similarity relation, gives a definition of lambda-discernibility matrix; secondly, an attribute reduct algorithm of decision table with continuous condition attributes is put forward; thirdly, an algorithm of computing significance of each condition attribute is put forward according to the properties of lambda-discernibility matrix; at last, the time complexity of the attribute reduct algorithm is analyzed and the rationality and effectiveness of this algorithm is accounted for through an example.

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What this paper is about

Firstly, the author generalizes the indiscernible relation to similarity relation, gives a definition of lambda-discernibility matrix; secondly, an attribute reduct algorithm of decision table with continuous condition attributes is put forward; thirdly, an algorithm of computing significance of each condition attribute is put forward according to the properties of lambda-discernibility matrix; at last, the time complexity of the attribute reduct algorithm is analyzed and the rationality and effectiveness of this algorithm is accounted for through an example.

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Available abstract

Firstly, the author generalizes the indiscernible relation to similarity relation, gives a definition of lambda-discernibility matrix; secondly, an attribute reduct algorithm of decision table with continuous condition attributes is put forward; thirdly, an algorithm of computing significance of each condition attribute is put forward according to the properties of lambda-discernibility matrix; at last, the time complexity of the attribute reduct algorithm is analyzed and the rationality and effectiveness of this algorithm is accounted for through an example.

Key concepts: Reduct, Attribute domain, Rough set, Variable and attribute, Relation (database), Decision table, Similarity (geometry), Algorithm

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