Rough set data analysis in the knowledge discovery in database (KDD) is different to other KDD methods, especially with respect to model assumption.
粗集数据分析不同于其它知识发现方法，特别在模型假设方面的一种方法。
Rough set data analysis doesn't need any other information but data set, simultaneously, the results obtained are usually short of statistical evidence.
粗糙集方法虽然不需要数据之外的其它信息，但所得结果同时也缺乏统计证据。
Four kinds of condition entropy are defined in this paper. Accordingly, four kinds of entropy based methods for the attribute reduction in the rough set data analysis are proposed.
本文定义了四种条件熵，并在此基础上提出了四种基于熵的方法，以用于粗糙集数据分析中的属性简约。
Rough set theory has been aiming at data analysis problems involving uncertain or imprecise information.
粗糙集理论一直致力于研究不确定或不精确信息的数据分析问题。
A rough set based data mining system named RSDMS is put forward and realized after the analysis of algorithms in rough set.
在分析了粗集的各种算法之后提出并实现了一个基于粗集的数据挖掘系统RSDMS。
Rough set, as a theory of data analysis, can deal with uncertainty efficiently , and is one of current hot research directions in knowledge discovery.
粗集作为一种数据分析理论，能有效地从不确定性的数据中发现知识，是目前在知识发现领域研究的热点之一。
On this basis, the thesis conducts in-depth analysis of Data Mining process based on Rough Set, then studies and analyses the algorithms used in these processes.
在理论的基础上，在研究数据挖掘的一般过程的基础上，深入分析了基于粗糙集的数据挖掘的过程，并对应用于这些过程的算法进行了研究和分析。
And the rough set is a new data analysis tool to deal with ambiguity and uncertainty of knowledge; it has been successfully applied to many areas of classification.
而粗糙集是一种新的处理模糊和不确定性知识的数据分析工具，已被成功地应用到许多有关分类的领域。
Attribute reduction, a basic conception in rough set theory, is introduced at first, then applied to forestry information management. The ability of data analysis is enhanced by this way.
本文介绍了粗糙集理论基本内容属性约简，并将其应用在林业信息管理中，通过实例说明提高数据分析能力的方法。
Rough set theory is a new data mining and decision analysis method. Knowledge reduction and decision rule mining in decision table by using rough set theory has become a research hotspot.
粗糙集理论是一种新型的数据挖掘和决策分析方法，利用粗糙集理论进行决策表的知识约简与决策规则挖掘已经成为研究热点。
Rough set, as a theory of data analysis, can deal with uncertainty efficiently , and is one of current hot research directions in knowledge discovery. This paper introduces rough set theory briefly.
粗集作为一种数据分析理论，能有效地从不确定性的数据中发现知识，是目前在知识发现领域研究的热点之一。
Rough set theory is a new mathematic approach to uncertain and vague data analysis.
粗集理论是一种新的处理含糊和不确定性问题的数学工具。
Based on an incomplete data analysis method of the rough set theory and the distinguish matrix, bring forward an improved ROUSTIDA algorithm.
基于粗糙集理论的不完备数据分析方法，以可辨识矩阵作为算法的基础，提出了一种改进的不完备数据分析方法。
Rough Set Theory is a new mathematical approach to uncertain and vague data analysis. It is, no doubt, one of the most challenging areas of modern computer applications.
作为处理不确定和含糊问题的新的数学方法，粗糙集理论对于现代计算机应用，无疑是最具挑战性的领域之一。
The analysis of the calculation examples shows that it is feasible to forecast the power load under the effect of data uncertainty by rough set theory.
算例分析表明，应用粗集理论解决数据不确定性影响下的电力负荷预测是可行的。
The relationship matrix between fault reasons and malfunction symptoms can be obtained by failure analysis and historical fault data and can be reduced by the application of rough set theory.
通过故障分析和历史故障数据得到故障原因-征兆相互关系矩阵，应用粗糙集理论约简关系矩阵。
It is researched that the consistent datum are disposed by rough set theory in the data fusion center of stocks analysis system.
将粗糙集理论应用于证券分析系统，探讨了相容状况下基于粗糙集理论的数据处理方法，并给出了系统结构和融合方法。
It is researched that the consistent datum are disposed by rough set theory in the data fusion center of stocks analysis system.
将粗糙集理论应用于证券分析系统，探讨了相容状况下基于粗糙集理论的数据处理方法，并给出了系统结构和融合方法。
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