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不完备信息系统的增量式约简算法
引用本文:金玲玲,王喜凤,朱紫焱.不完备信息系统的增量式约简算法[J].辽宁工程技术大学学报(自然科学版),2012(2):284-288.
作者姓名:金玲玲  王喜凤  朱紫焱
作者单位:1. 海南师范大学数学与统计学院,海南海口571158
2. 安徽工业大学计算机学院,安徽马鞍山243002
3. 郑州大学科研处,河南郑州450001)
基金项目:海南省自然科学基金资助项目(610221);海南师范大学青年教师科研启动基金资助项目(QN0918)
摘    要:针对经典粗糙集模型在处理不完备、动态数据方面的不足,通过分析容差关系模型,引入先验概率在知识估计中的方法,给出了一种基于区分矩阵的增量式属性约简算法.以属性重要度为启发信息,对区分矩阵的构造过程进行改进,仅需简单的矩阵运算就可以得到约简结果.最后通过示例分析处理增量式数据的算法复杂度有效,算法正确可行.

关 键 词:粗糙集  不完备系统  增量式约简  区分矩阵  属性重要度  先验概率  容差关系  算法复杂度

Incremental reduction algorithm based on imcomplete information system
JIN Lingling,WANG Xifeng,ZHU Ziyan.Incremental reduction algorithm based on imcomplete information system[J].Journal of Liaoning Technical University (Natural Science Edition),2012(2):284-288.
Authors:JIN Lingling  WANG Xifeng  ZHU Ziyan
Institution:1.School of mathematics and statistics,Hainan Normal University,Haikou 571158,China; 2.School of Computer Science,Anhui University of Technology,Ma’an shan 243002,China; 3.Research Department,Zhenzhou University,Zhenzhou 450001,China)
Abstract:Aiming at the disadvantage of classical rough set model in dealing with imcomplete and dynamic data,a prior probability method in estimating knowledge is introduced through analyzing tolerance relation,and a incremental attribute reduction algorithm based on discernibility matrix is proposed.Using attribute significance as heuristic message,the process of constructing discernibility matrix is improved,reduction result can be got only by simple matrix computing.Finally,algorithm’s complexity in dealing with incremental data is effective through example analysis,and the algorithm is valid and feasible.
Keywords:rough set  imcomplete system  incremental reduction  discernibility matrix  attribute significance  prior probability method  tolerance relation  algorithm’s complexity
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