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In this paper,we present a heuristic reduced algorithm,denoted RedFreSigni,that satisfies the attribute significance and attribute frequency at same time.This algorithm is based on the algorithms of attribute significance and resolution matrix.It takes the attribute′s core and user′s preference set as part of the attribute reduction,and using frequency as the heuristic information of attribute selection,and creating the frequency information of calculation attributes and undistinguishable... In this paper,we present a heuristic reduced algorithm,denoted RedFreSigni,that satisfies the attribute significance and attribute frequency at same time.This algorithm is based on the algorithms of attribute significance and resolution matrix.It takes the attribute′s core and user′s preference set as part of the attribute reduction,and using frequency as the heuristic information of attribute selection,and creating the frequency information of calculation attributes and undistinguishable matrix simultaneously,so the calculating time is reduced.Accordingly,a decision mining algorithm is presented which is based on rulesupport and confidence.Users can extract the useful rules effectively by using this algorithm. 在基于属性重要性和基于分辨矩阵两种算法的基础上,提出了一种同时满足属性重要性和频度的启发式约简算法RedFreSigni。该算法的基本思想是:以属性的核为基础,把核和用户偏好集同时作为属性近似约简的一部分,以频度作为选择属性的启发信息可同时生成计算属性的频度信息与不可分辨矩阵,减少了计算时间。在此基础上进而提出了基于规则支持度和置信度的决策挖掘算法,该算法能有效提取出用户感兴趣的规则。 We studied the heuristic reduced algorithm based on the separating capacity,proposed in paper~([1]),and it is useful to deal with the noise.We think we can improve something not so prefect,just as the knowledge quantification,the proof of the quantifying rationality,the algorithm they proposed and the process proved by example.We proposed some methods to improve theirs,and we also used example to prove them.We proved that the knowledge quantification after the improvement is correct.We proved that... We studied the heuristic reduced algorithm based on the separating capacity,proposed in paper~([1]),and it is useful to deal with the noise.We think we can improve something not so prefect,just as the knowledge quantification,the proof of the quantifying rationality,the algorithm they proposed and the process proved by example.We proposed some methods to improve theirs,and we also used example to prove them.We proved that the knowledge quantification after the improvement is correct.We proved that the proof of the quantifying rationality after the improvement is correct.We proved the reduced improved algorithm referring the knowledge quantity as the heuristic function and the process proved by example are correct. 通过对徐燕等提出的能有效处理噪音的基于区分能力大小的启发式约简算法的研究,认为所提出的对知识进行量化、证明量化的合理性、给出的算法和实例证明的过程中还有一些不完善的地方,需要进行修正.该文提出了修正方法,并通过实例证明了修正后的算法对知识进行量化、证明量化的合理性、以知识量为启发函数的约简修正是正确的.
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