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2010•Journal of Henan Normal UniversityRequires access

A Reduction Algorithm Based on Relative Granularity in Incomplete Decision Tables

Shi Jin-lingb

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Abstract

To effectively extract the minimal relative reduction from incomplete decision table,the paper proposes an attribute reduction algorithm based on granular computing.First,changing trends of relative granularity in the process of the attribute reduction is analyzed and studied.Secondly,by increasing the attribute into the nuclear attributes set,method of extracting minimal reduction from incomplete decision table is discussed.Last,a detailed example is given to prove the validity of the algorithm.

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What this paper is about

To effectively extract the minimal relative reduction from incomplete decision table,the paper proposes an attribute reduction algorithm based on granular computing.First,changing trends of relative granularity in the process of the attribute reduction is analyzed and studied.Secondly,by increasing the attribute into the nuclear attributes set,method of extracting minimal reduction from incomplete decision table is discussed.Last,a detailed example is given to prove the validity of the algorithm.

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Available abstract

To effectively extract the minimal relative reduction from incomplete decision table,the paper proposes an attribute reduction algorithm based on granular computing.First,changing trends of relative granularity in the process of the attribute reduction is analyzed and studied.Secondly,by increasing the attribute into the nuclear attributes set,method of extracting minimal reduction from incomplete decision table is discussed.Last,a detailed example is given to prove the validity of the algorithm.

Key concepts: Granularity, Reduction (mathematics), Decision table, Granular computing, Computer science, Algorithm, Set (abstract data type), Table (database)

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