数据约简是粗糙集理论中一个非常重要的研究课题。
Data reduction is one of important research issue in rough set theory.
通过系统聚类和粗糙集两种方法进行数据约简，使数据得到横向和纵向两个方向上的约简。
The data are reduced in both horizontal and vertical directions by using hierarchical clustering and rough set methods.
粗糙集理论的概念性框架之一就是利用不可分辨关系和布尔推理作为数据约简和获取决策规则的基础。
Rough sets theory provides a framework in which indiscernibility relations and Boolean reasoning form a foundation for data reduction and decision rule generation.
文中首先阐述了软件测试相关的基本概念，然后介绍了粗糙集理论基本概念和基于粗糙集理论的数据约简。
The prime concept of software testing is presented, then introduces the prime concept of rough set theory and the data reduction method based on rough set.
与其它数据约简方法相比，这种方法直接源于评审数据，思路清晰，拟合结果表明了本约简算法合理、可靠性强。
Compared with general methods, the result attained by this method rooted in evaluation data directly, so it has more reliability and strongly persuasion.
提出了基于分辨矩阵和数据分析的两种数据约简模型，并对分辨矩阵算法做了改进，最后对这两种模型进行了比较。
It proposes two data reduction models based on discernibility matrix and data analysis separately, improves the discernibility matrix algorithm and made the comparison between the two models.
针对小波变换无法利用光谱数据与组份含量之间关系来压缩数据的缺点，提出了基于粗糙集的进一步数据约简方法。
Based on the shortcoming of wavelet transform that it can't analyze the relations between spectra and contents, a method based on rough set is presented.
给出了数据约简方法，包括建立RS知识模型、RS决策逻辑表示、确定辨识矩阵、计算其核心、进行属性归约、规则形成等内容。
Information reduction method is given, in which includes RS knowledge modeling, RS decision logic expression, distinction matrix making, cores calculation, attributes reduction, rules generation, etc.
介绍了粗糙集数据约简概念，包括相对约简和绝对约简，并将它们统一为差别列表上的集合操作，其中差别列表是从差别矩阵引伸而来的。
The problems of data reduction, including relative reduction and absolute reduction are introduced and unified as the set operation on difference list that is come from the difference matrix.
算法具有多边形约简算法相同的优良的近似质量，并可在固定数据缓冲区空间内在线运算。
The quality of approximation is as good as polygonal boundary reduction, and it can process the data online in a constant buffer.
包括理论的提出，一些基本的概念，数据的约简，知识表达系统，属性的约简，决策逻辑和决策规则最小化等。
It includes the proposing of the theory, some relevant fundamental concepts, the reduction of data and attributes, KRS, decision analysis, and the reduction of decision rules.
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。
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.
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。
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.
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。
Then the author's researches on data discretization and attribute reduction are introduced in detail.
讨论了整数到二进制数据的转换，并在此基础上实现了二进制信息系统的约简。
This paper discuss the converting given integer data into binary data, and reducing of binary information systems base on it.
知识发现的过程包括时间序列数据预处理、属性约简和规则抽取三部分。
The process of knowledge discovery in time series includes preprocessing of time series data, attributes reduction and rules extraction.
属性约简是粗糙集用于数据分析的重要概念。
Attribute reduction is an important concept in rough sets data analysis.
本论文主要讲述数据挖掘中采用粗糙集方法实现数据预处理中冗余属性约简的问题。
This paper mainly discusses topics on solving attribute reduction problems by applying rough set methods in the field of scientific data mining.
属性约简是数据挖掘领域中的核心内容之一。
Reduction of attributes is one of the major concerns in the field of data mining.
针对决策表中属性取值为杂合数据的情况，提出了基于粗糙集理论的属性约简算法。
With regard to the attribute values in decision table, which are described with hybrid data, a new algorithm of attribute reduction based on rough set theory is proposed.
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。
This model including three modules: the data pretreatment, the attribute reduction and the rule extraction, then confirms this model's feasibility using the example.
本文重点研究了在不完备信息系统中数据动态变化情况下的属性约简问题，针对已有算法提出了改进的算法。
This paper proposed an improved algorithms in incomplete information system data in the context of dynamic changes of attribute reduction for the existing algorithms.
把粗糙集理论与基于概率统计ID3算法结合建立粗糙集约简模型，可处理不精确和模糊数据集信息。
The rough sets reduction model is established by integrating rough sets theory with ID3 algorithm based on statistics, uncertainty fuzzy data set information can be processed with the model.
利用粗糙集理论对原始数据进行约简，构建优化的粗糙集—神经网络智能系统。
The reduction of original data based on rough-set theory is derived and the optimized intelligent system with rough-set neural network is established in this paper.
将属性值约简和数据挖掘相结合，给出支持度、置信度、覆盖度的定义。
This paper associates attributive value reduction with data mining and proposed three concepts: support, confidence and coverage.
信息系统的属性约简反映了一个决策表的本质信息，为信息系统的数据挖掘奠定的基础。
Attribute reduction of information system is to remove superfluous attributes from information systems while preserving the consistency of classifications the original system provides.
信息系统的属性约简反映了一个决策表的本质信息，为信息系统的数据挖掘奠定的基础。
Attribute reduction of information system is to remove superfluous attributes from information systems while preserving the consistency of classifications the original system provides.
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