Computer Science ›› 2015, Vol. 42 ›› Issue (8): 259-264.

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Global Positive Region Inconsistency Based Attributes Core Computation

ZHAO Jie, LIANG Jun-jie, DONG Zhen-ning, CHEN Xu and TANG De-yu   

  • Online:2018-11-14 Published:2018-11-14

Abstract: This paper firstly proposed basic algorithms of positive region and equivalence class based on bit vector and improved Hash algorithm.Then the core attributes computation algorithm was designed based on global positive region inconsistency.Different from current algorithms which need to compute complete positive regions repeatedly when seeking attributes core,this paper studied the characteristics of core attributes,and caught the inconsistencies between the positive region of C-{ai} and the global positive region.The complete positive regions of C-{ai} don’t need to be computed repeatedly.Global positive region inconsistency based attributes core recognition was proved by 3 theories.21 data sets of UCI,ultra-high-dimensional data sets and massive data sets were used to test the algorithms proposed by this paper.And the results show the attributes core computation algorithm of this paper owns good performance no matter when the number of entities and attributes is more or less and especially is suitable for processing large decision table.

Key words: Rough set,Attributes core,Global positive region,Inconsistency

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