模糊粗糙集理论是解决数据集维数问题的有效工具，但基于模糊粗糙集的降维算法还不多。
Fuzzy rough set theory is an effective tool for reduction of data dimension, but there are few dimension reduction algorithms that are based on fuzzy rough set theory so far.
该算法利用模糊粗糙集理论，依照图像的视觉特性，采用传统的图像增强方法进行图像预处理。
The algorithm makes use of fuzzy-rough sets theory to describe its uncertainty degree and carries through its pre-processing with traditional methods according to the image's visual property.
应用这种方法能够将不完备系统转化为模糊信息系统，然后利用模糊粗糙集理论，让隐藏在不完备信息系统中的知识以决策规则的形式表示出来。
Based on fuzzy rough set theory, it is liable to change the incomplete system into fuzzy information system and make the covert knowledge stand out in a way of decision regularity.
粗糙集理论作为一种处理模糊和不确定性问题的有效工具，对时间序列的数据挖掘是有效的。
Rough set theory, as an effective tool to deal with vagueness and uncertainty, is effective to the time series data mining.
粗糙集理论是处理模糊和不确定性问题的新的数学工具。
Rough set theory is a novel mathematical tool dealing vagueness and uncertainty.
提出了一种基于模糊软分类和粗糙集理论来提取模糊规则的一种算法。
On the basis of fuzzy clustering and rough set, an algorithm for extracting fuzzy rules was proposed.
粗糙集理论是继概率论、模糊集、证据理论之后的又一个处理不确定性问题的新型数学工具。
Rough set theory is a new mathematical tool to deal with vagueness and Uncertainty problem after probability theory, fuzzy sets, mathematical theory of evidence.
粗糙集理论是一种处理模糊和不精确知识的数学工具，它具有很强的知识获取能力。
Rough Set is a new mathematical tool to deal with fuzzy and uncertain knowledge. It has strong knowledge obtaining ability.
借助模糊聚类技术和粗糙集理论提出了一个基于客观信息熵的多因素权重分配方法。
Using fuzzy aggregation theory and rough set theory, this article puts out a weight allocation method based on impersonal message entropy.
粗糙集理论是一种新的处理模糊和不确定性知识的数学工具，在人工智能及数据挖掘等众多领域已经得到了广泛的应用。
Rough set theory is emerging as a powerful tool for dealing with vagueness and uncertainty of facts, which has important applications to artificial intelligence and data mining.
粗糙集理论是一种新的处理模糊和不确定性知识的软计算工具，在人工智能及认知科学等众多领域已经得到了广泛的应用。
Rough set theory is emerging as a powerful tool for dealing with vagueness and uncertainty of facts, which has important applications to artificial intelligence and cognitive science.
进而，据此开发了一个基于信息分类的产品编码与配置系统，并提出了利用粗糙集的相关理论与方法处理配置过程中的客户模糊需求信息的方法。
Moreover, the product coding and configuration system based on information classifying was developed by using the theory of Rough Set to deal with uncertain customer demand.
粗糙集理论是1982年提出的一种处理模糊和不确定知识的数学工具。
Rough set theory, proposed in early 1982, is a new mathematical tool in dealing with imprecision and uncertainty.
粗糙集理论是一种处理模糊、不确定知识的工具。
Rough Set theory is a kind of new tool for dealing with vagueness and uncertainty of knowledge.
提出应用粗糙集理论辩识模糊隶属函数的可行性。
The feasibility of applying theory of rough sets to the identification of fuzzy membership function is confirmed in this paper.
该方法很好的结合了模糊聚类法和粗糙集理论，对知识的模糊性以及相关信息获取及处理的弊端都进行了修正。
The method revises the knowledge fuzzy and the abuse of informationaccessing and processing by combining the theory of rough sets with fuzzy clustering approach.
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。
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.
数据挖掘是知识发现领域的一个重要问题，粗糙集理论是一种具有模糊边界的数据挖掘方法，它被广泛应用于决策系统的分类规则提取中。
Data mining is an important problem in KDD, and Rough set as a theory of set with fuzzy boundary is widely applied to infer classification rules from decision system.
首先本文将粗糙集理论与模糊集理论进行比较，通过粗糙隶属函数将模糊集的研究方法引入到粗糙集的研究中。
In this paper, firstly we compare RST with fuzzy Sets Theory and introduce fuzzy method into the study of RST by the rough membership function.
将粗糙集理论与模糊逻辑技术相结合，提出了一种通过测量数据来获取模糊控制规则的方法。
Combining rough set theory with fuzzy logic technology, this paper has presented a method of gaining fuzzy control rules based on measured data.
在粗糙集理论及粗糙模糊集理论中，上下近似及边界的求解与决策表属性约简是它们的核心内容。
In the rough set theory and rough-fuzzy set theory, computation of approximations and edge and attributes reduction of decision table is import part of them.
粗糙集理论是一种新型的处理模糊和不确定知识的数学工具，它能在保持信息系统分类能力不变的前提下，有效地进行知识约简；
Rough sets theory is a new mathematical tool to deal with vagueness and uncertain, which can remove redundant information and seek for reduced decision tables effectively.
提出了用粗糙集理论构造模糊多层感知器的方法。
A method of constructing knowledge based fuzzy perceptron based on rough sets theory is proposed.
通过粗隶属函数，将粗糙集理论与模糊集理论联系起来，建立一种粗糙集理论与模糊集理论间的关系。
We combine the fuzzy set theory with rough set theory by rough membership function and establish a relation between them.
针对流程工业数据的高维数、不确定的特点，研究适合处理流程数据的模糊集、粗糙集的粒度数据挖掘理论和方法。
According to the high dimensions and uncertainty of process industrial data, the fuzzy set and rough set of granularity data mining are studied for process data.
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.
详细介绍了数据挖掘技术的常用方法，包括模糊理论、粗糙集理论、云理论、证据理论、人工神经网络、遗传算法以及归纳学习。
Mostly used methods are introduced in detail, including fuzzy method, rough sets theory, cloud theory, evidence theory, artificial neural networks, genetic algorithms and induction learning.
详细介绍了数据挖掘技术的常用方法，包括模糊理论、粗糙集理论、云理论、证据理论、人工神经网络、遗传算法以及归纳学习。
Mostly used methods are introduced in detail, including fuzzy method, rough sets theory, cloud theory, evidence theory, artificial neural networks, genetic algorithms and induction learning.
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