This paper introduces a new algorithm for reduction of attribute.
本文提出了一种新的算法用于粗集中的属性约简。
Based on the reduction of attribute sets, the partial order relation matrix of radar jamming space is obtained.
在计算属性约简集的基础上，建立了雷达干扰空间的偏序关系矩阵。
This algorithm takes the importance of attribute as the iterative criterion and finds the least reduction of attribute-set.
该算法以属性重要度为迭代准则得到属性集合的最小约简。
Secondly, during reduction of attribute values, some irrelevant attributes are further eliminated, then abstractation of personalized decision rules is accomplished.
然后在属性值约简中进一步去除与用户无关的属性，从而抽取个性化决策规则。
Then, considering the practical requirements of team combat, a method for reduction of attribute-values under single decision attribute is extended to the reduction under multi-decision attributes.
并将单一决策属性下的属性值约简方法推广，讨论了更适于编队作战分析的多决策属性下的属性值约简问题。
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。
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。
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。
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。
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。
It main using to seek the relative of attribute reduction.
主要用于求解决策表中的相对属性约简。
In this paper, a novel decision table discretization algorithm is presented, which has fine attribute reduction function in time of data discretization and increases quality of classification.
本文提出了一个新的决策表离散化算法，该算法在离散化数据的同时具有良好的属性约简功能。
Attribute reduction is one of important step in preprocessing of data mining, it improves the efficiency of the data mining algorithm by reducing the dimensions of the information.
属性约简是数据挖掘预处理中非常重要的一步，它通过减少信息的维数提高数据挖掘算法的效率。
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。
The Rough Set Theory can handle such problems as data reduction, data mining, the evaluation of attribute importance, the formation of decision algorithm etc.
利用粗集理论处理的主要问题包括：数据简化、数据相关性的发现、数据意义的评估、由数据产生决策算法等。
With all the condition attribute as the initial reduction, this algorithm takes importance of attribute as the iterative criterion to find reduction.
该算法以所有条件属性为初始约简集合，以属性重要性为迭代准则，通过逐步缩减来求取约简。
A kind of attribute relative reduction for decision attribute support degree was proposed.
提出了一种基于决策属性支持度的属性相对约简算法。
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。
So a reduction algorithm of attribute for personalized knowledge discovery was designed.
为此，设计了一种面向个性化知识发现的属性约简算法。
Acquiring optimal relative reduction by descending approach to core of attribute from original set of conditional attribute and combining with operator.
以原始条件属性集为起点并结合算子，通过向属性核的递减式逼近，得到属性的最小相对约简。
Then, we present an improved decision matrix together with a method for attribute reduction of the decision table and an example shows that the improved method is effective and complete.
然后给出一个改进的决策矩阵和属性约简方法，例子分析表明，改进后的方法是有效的和完备的。
A kind of attribute relative reduction for fuzzy decision attribute dependent degree is proposed.
提出了一种基于模糊决策属性依赖度的属性相对约简算法。
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。
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。
Firstly, we put forward an improved domain reduction method on the basis of analyzing the attribute reduction algorithm of the current information system.
首先分析对比较为成熟的属性约简算法的优缺点，提出论域缩减的方法。
The item of attribute frequency in the scan vector was taken as heuristic information to improve the efficiency of attributes reduction.
以扫描向量中的属性频率项作为属性约简搜索的启发信息，提高了属性约简效率。
Knowledge reduction is the core of Rough set. It obtains the reduction attribute and classification rules while holding the ability of classification unchanged.
知识约简是其中的核心内容，是在保持分类能力基本不变的情况下，获得系统的约简属性和分类规则。
In this paper, the attribute reduction of rough set is discussed from the viewpoint of granular computing, the concepts of granularity is defined and a new attribute reduction algorithm is presented.
该文从粒度计算的角度对粗糙集理论的属性约简进行研究，定义了粒度的概念，并在此基础上提出了一种新的属性约简算法。
The technology of attribute reduction based on the intuitionistic fuzzy rough set theory is described as to the problem of information loss in the process of discretization.
本文针对传统的离散化技术所造成的信息丢失问题，提出了利用直觉模糊粗糙集合理论来进行属性约简的方法。
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。
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