Then, an attribute reduction algorithm based on indiscernibility matrix is introduced.
给出了基于不可区分矩阵的属性频率约简算法。
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。
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。
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。
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 general algorithm of attribute reduction and value reduction is presented, from which the attribute reduction algorithm based on information is proposed.
并给出了粗糙集理论中属性约简以及值约简的常规算法，在此基础上给出了基于信息量的属性约简算法。
After that we study on the ordered decision table and propose a new heuristic attribute reduction algorithm based on dominance matrix, whose time complexity is polynomial.
再次，对有序决策表进行了研究，提出了一种基于优势矩阵的启发式属性约简算法。
The paper offer a new heuristic attribute reduction algorithm based on conditional granularity entropy, though running an example, we show that this algorithm is effective.
提出了一种基于条件粒度熵的属性约简的启发式算法，通过例子分析，表明该算法是有效的。
In incomplete information system, objects of the tolerance class and maximal consistent block in the attribute reduction algorithm based on discernibility matrix are uncertain.
在不完备信息系统基于差别矩阵的属性约简算法中，相容类和最大相容类中的对象具有不确定性。
This paper discussed the attribute reduction in rough set combined fuzzy relation theory, and then proposed a new attribute reduction algorithm and gave an illustrative example.
结合模糊关系的理论，对粗糙集理论的属性约简算法进行研究，提出了一个新的属性约简算法，并给出了一个应用实例。
To combine the attribute reduction algorithm and the incremental training algorithm of support vector machine, a support vector machine classifier based on rough set is constructed.
将属性约简算法和支持向量机增量训练算法相结合，构造基于粗糙集数据预处理的支持向量机分类器。
The comprehensive method firstly used the new document frequency to select features to filter out some terms, and then employed the attribute reduction algorithm to eliminate redundancy.
该方法首先利用新型文档频进行特征初选以过滤掉一些词条，然后利用所提属性约简算法消除冗余。
This text does the following research for attribute reduction and application on the rough set of variable precision. Study attribute reduction algorithm of variable precision rough set.
本文在变精度粗糙集的属性约简及应用方面做了如下主要研究：研究了变精度粗糙集的属性约简算法。
And three modified attribute reduction algorithms are presented, including modified algebraic algorithm, weighed sum of attribute significance algorithm and modified discernible matrix algorithm.
提出了三种改进的属性约简算法：改进的代数集合算法、重要度加权平均算法和改进的可辨识矩阵算法。
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。
Based on the definitions, the core finding algorithm, the relative attribute reduction algorithm and value reduction of information decision system are presented based on binary discernibility matrix.
在定义的基础上，给出了基于二进制区分矩阵的求核算法、相对属性约简算法及值约简算法。
This paper improves the attribute reduction algorithm by rewriting the attribute significance which brings in the expert knowledge in the basis of not changing the ability of rough set classification.
本文在不改变粗糙集分类能力的基础上引进专家知识，通过改写属性重要度来改进属性约简算法。
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。
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。
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。
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。
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。
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。
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。
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。
The new algorithm carries out the reduction processing to the generated decision rules containing the frequency attribute and obtains the simplest decision rules.
新的算法对生成的带频度属性的决策规则进行约简处理，得出最简决策规则。
In this paper, we propose a new attributes reduction algorithm based on the significance of attribute dependencies as heuristic information and add a certain variable precision.
粗糙集理论是一种新的数据挖掘算法，文章以属性依赖重要性作为启发信息提出了一种新的属性约简算法，且加入了一定的分类正确度。
In this paper, we propose a new attributes reduction algorithm based on the significance of attribute dependencies as heuristic information and add a certain variable precision.
粗糙集理论是一种新的数据挖掘算法，文章以属性依赖重要性作为启发信息提出了一种新的属性约简算法，且加入了一定的分类正确度。
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