Through examples, it shows that attribute reduction of an inconsistent decision table cannot entirely be represented by conditional information quantity.
并举例说明，对于不一致决策表，其属性约简的代数表示不能用条件信息量来等价表示。
To a certain extent, the algorithm can resolve the attribute reduction problem of abnormal decision tables and extend application of rough set theory.
该算法在一定程度上能够解决非常规决策表的属性约简问题，进一步扩展了粗糙集理论的应用范围。
Conduct attribute reduction and value reduction.
将决策表进行属性约简，值约简。
The attribute reduction is one of the cores of Rough Set (RS) theory.
属性约简是粗糙集(RS)理论的核心内容之一。
Firstly, a relation information system and its attribute reduction are defined.
首先定义了关系信息系统及其属性约简的概念。
Proposed an algorithm of attribute reduction based on attribute importance of rough set.
提出一种基于粗糙集属性重要性的属性约简算法。
And then a relative attribute reduction algorithm is mentioned based on information entropy.
后面又提到了基于信息熵的相对属性约简算法。
The process of attribute reduction is analyzed in assembly knowledge discovery based on rough set.
分析了基于粗糙集理论的装配知识发现中属性归约的过程。
In the theory of rough set, attribute reduction of decision table was an important research subject.
在粗糙集理论中，决策表的属性约简是一个非常重要的研究课题。
Then the author's researches on data discretization and attribute reduction are introduced in detail.
随后论文重点对作者在数据离散和属性约简两个方面做的研究工作进行了阐述。
This paper discusses algebra properties and changing law of entropy for some attribute reduction definitions.
讨论几种属性约简定义的代数性质和信息熵改变规律。
By using attribute reduction rule, it can distinguish the redundant knowledge and effectively discover key attributes.
应用属性约简规则处理数据可有效识别冗余知识，找出关键属性；
In this paper we present a novel hybrid algorithm based on attribute reduction of RS and classification principles of SVM.
结合粗糙集的属性约简和支持向量机的分类机理，提出了一种混合算法。
To solve the inefficiency of data mining, application of attribute reduction algorithm in CRM data preparation is studied.
针对银行CRM中的数据冗余大、数据挖掘效率低的问题，将基于属性约简的数据预处理方法应用在银行CRM中。
And on this basis, an attribute reduction algorithm based on the improved differential evolutionary algorithm was put forward.
并在此基础上提出了基于差分演化算法的属性约简算法。
For the sake of different applications, various definitions of attribute reduction have been proposed by different researchers.
出于应用目的，许多学者提出了各种不同的属性约简概念。
The time complexity and space complexity of the traditional attribute reduction algorithm using discernible matrix are quite big.
传统的利用区分矩阵进行属性约简算法，其时间复杂度和空间复杂度很大。
To improve the efficiency of attribute reduction, a rapid reduction algorithm based on conditional information quantity is proposed.
为提高粗集约简的效率，提出了一种基于条件信息量的快速粗集约简算法。
Finally, the equivalence properties between Boolean matrix representation and algebra representation of attribute reduction are proved.
最后证明了属性约简在布尔矩阵和代数两种不同表示下是等价的。
Based on the attribute reduction of rough set, from different aspects, this paper sets up indicator screening model based on rough set.
基于粗糙集的属性约简原理，本文从不同的角度建立了基于粗糙集的指标筛选模型。
The attribute reduction quality, which includes reduction ratio and approximate quality, is defined to scale the reduction effectiveness.
提出约简质量的定义，从属性约简率和近似质量两方面来衡量约简效果。
But the attribute reduction is a NP problem, the attribution reduction and decision rule reduction will be solved by method of elicitation.
但是属性约简是一个NP问题，对属性的约简和决策规则的约简只能通过启发式算法实现。
Under the condition of unchanged classification and decision abilities, attribute reduction is to delete irrelative or unimportant attribute.
属性约简要求在保持知识库的分类和决策能力不变的条件下，删除不相关或不重要的属性。
Through examples, it shows that attribute reduction of an inconsistent decision table cannot entirely be represented by information quantity.
并举例说明，对于不一致决策表，其属性的约简不能用信息量来等价表示。
In data mining with Attribute-Oriented generalization (AOG), attribute reduction and concept generalization can not part from concept hierarchy.
在面向属性归纳(AOG)的数据挖掘中，属性归约及概念归纳都离不开概念层次。
The existing researches on generalized rough sets are mainly concentrated on set approximations; less effort has been made for attribute reduction.
目前，人们对广义粗糙集的研究主要集中在集合的近似计算上，而真正利用广义粗糙集进行属性约简的研究还很少见。
To predict reservoir characteristic parameters of four regions exactly, a method based on the attribute reduction by the rough set and SVR is presented.
为了更准确的预测油藏四个区域的物性参数，本文提出了结合粗糙集属性约简和支持向量机回归的方法。
This model including three modules: the data pretreatment, the attribute reduction and the rule extraction, then confirms this model's feasibility using the example.
该模型包括数据预处理、属性约简和规则提取三个模块，并利用算例验证该模型的可行性。
With the combination of attribute reduction in concept lattice, the model provides users with a semantic matching model based on concept lattice of theoretical value.
结合概念格属性约简等知识，该模型为用户提供一种有价值的基于概念格的语义匹配方法。
With the combination of attribute reduction in concept lattice, the model provides users with a semantic matching model based on concept lattice of theoretical value.
结合概念格属性约简等知识，该模型为用户提供一种有价值的基于概念格的语义匹配方法。
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