Relative Granularity Incremental Attribute Reduction Algorithm Based on Relational Matrix

Abstract

Abstract: Attribute reduction is an important data processing method in rough set and granular computing theory. In view of the
shortcomings of traditional attribute reduction algorithms in dealing with dynamic data problems and based on relative granularity, the
incremental change mechanism of relative granularity changes is studied using relational matrix when the objects of the decision
information system change. According to this change mechanism, a relative granularity incremental attribute reduction algorithm based on relational matrix is proposed to improve the efficiency of attribute reduction.

Key words: , relational matrix, relative granularity, attribute reduction

CLC Number:

TP18

Wang Jihe. Relative Granularity Incremental Attribute Reduction Algorithm Based on Relational Matrix[J]. Journal of Dali University, 2024, 9(12): 30-35.

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