Computer Science ›› 2017, Vol. 44 ›› Issue (1): 226-234, 258.doi: 10.11896/j.issn.1002-137X.2017.01.043

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Granularity of Rough Equivalence Class Based Incremental Attribute Core Computation Using Multiple Accelerating Strategies Pruning and Multiple Hashing

ZHAO Jie, ZHANG Kai-hang, DONG Zhen-ning, LIANG Jun-jie and XU Ke-fu   

  • Online:2018-11-13 Published:2018-11-13

Abstract: A new incremental core computation algorithm was proposed.Firstly,rough equivalence class(REC) was proposed based on the smallest computational granularity of global equivalences,the character of REC was analyzed and core and reduction computation under REC was studied.Then relationship of core attributes and REC were studied and then an equal method of judging core attribution and incremental core computation method based on 0-REC were designed,through which multiple non-core attributions can be gained in one calculation.Based on which,bilateral pruning strategies were proposed to reduce calculation field of both attributes and entities,so it need not travel all the attributes and entities.The pruning strategies still work even there is no core.At last,20 decision sets of UCI,massive and ultra-high dimension were used to verify the strategies and algorithms.The results show that the algorithm is effective and efficient,and in most conditions,the algorithm of this paper is superior to the current algorithms,and fit for massive decision table especially.The algorithm can be the basis of new reduction and optimization algorithms.

Key words: Reduction under rough set,Rough equivalence class,Incremental core computation,Hash

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