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.
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.
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.
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.
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.
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.
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.
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。
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.
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.
属性约简是对大数据集进行数据处理的需要。
It is the need of data processing in attribute reduction about large datasets.
本文主要研究基于粗集属性约简的数据挖掘系统。
This paper researches a data mining system based on attributes reduction of rough set theory.
应用属性约简规则处理数据可有效识别冗余知识，找出关键属性；
By using attribute reduction rule, it can distinguish the redundant knowledge and effectively discover key attributes.
利用粗糙集理论进行数据挖掘，抽取知识规则，最重要的一点就是基于粗糙集的属性约简和规则提取算法的研究。
To use of rough set theory for data mining and the extraction rules of the knowledge, the most important point is that based on the attribute reduction and rule extraction algorithms of rough set.
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.
本文研究了基于遗传算法和社会演化算法的数据挖掘和文本挖掘方法，主要包括数据挖掘和文本挖掘中的属性约简问题、聚类问题。
Several methods of data mining and text mining have been studied in this paper, which mainly includes: attribute reduction methods, clustering methods.
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.
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。
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.
其主要内容包括近似集、决策系统、数据预处理以及属性约简等等，是一种处理不完整，不精确数据的有效方法。
The main contents include the approximation set, decision systems, data preprocessing and attribute reduction and so on. It is a effective method of dealing with incomplete, inaccurate data.
利用粗糙集的属性约简性来约简掉一些冗余属性，提高了支持向量机进行数据处理的实时性，缩短了训练样本的时间。
That using the reduction attributes of rough set reduced some redundant attributes, improved the real time of data processing by support vector machine, and shorten the time for training sample.
另外，论文还就数据挖掘过程中应用粗糙集进行属性约简以简化算法复杂度做了一定的探讨。
Besides, this thesis makes a simple discussion on using the theory of Rough Set to reduce attribute, in order to simplify the complexity of the algorithm.
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。
To solve the inefficiency of data mining, application of attribute reduction algorithm in CRM data preparation is studied.
通过属性约简技术对神经网络的输入属性空间进行约简，采用神经网络对约简后的数据进行挖掘。
By reduction processing to the import space, this method adopts artificial neural network for data mining on the reduced training data.
粗糙集理论是机器学习和数据挖掘领域的重要课题之一，其中属性约简算法是该理论实现应用的主要算法。
Rough set theory is one of the main subjects in the field of machine learning and data mining.
对收集到的入侵数据进行预处理、数据离散化，属性约简，并依据生成的检测规则来分析入侵数据。
The algorithm includes data preconditioning, data discretization, attribute reduction, production of detection rules, and finally analysis of intrusion data with these rules.
遵循数据挖掘流程，详细介绍了数据属性的约简，模型建立以及模型评估的方法。
According to the data mining process, the procedure of data modeling, evaluation and data attribute reducing was deeply discussed.
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