查询词典 in rough

Rough Set theory is an useful tool to process inaccurate data. But classical rough set theory is not good enough to solve these problems when original data is fuzzy or indefinite.

但是在实际生活中，存在许多模糊现象不能简单地用传统理论精确地刻画，如何表示和处理这些现象是数据挖掘研究中的重要问题。

Considering that the classical rough set theory can only process the discrete data, the degree of general importance of an attribute and attribute subsets was presented. And then a generalization rough set theory was proposed based on the general near neighborhood relation.

针对经典粗糙集理论仅能处理离散化数据的局限性，提出属性和属性子集的广义重要度的概念以及空间中的广义近邻关系，并提出了广义近邻关系下的广义粗糙集扩展模型。

Based on the conditional rough entropy theory, the definition of the conditional rough entropy of set is given and it can measure the uncertainty of set. The properties and the related theorems are given.

基于知识的条件粗糙熵理论，提出了集合的条件粗糙熵概念，用来刻画和度量集合的不确定性。

The theory significance of conditional rough entropy in decision information system is discussed. The conditional rough entropy measures the partial uncertainty in decision information systems.

讨论了集合的条件熵在目标信息系统中的理论意义，集合的条件熵刻画了目标信息系统的局部的不确定性。

At first, analyze the farmer's desire for the pension by Rough Set theory, construct the attribute reduction algorithm based on the conditional entropy for the computation and analysis of 8,410 questionnaire surveys data to make use of the advantage of rough set to deal with indefinite and incomplete data.

首先利用粗糙集理论理不确定和不完整数据的优势，基于改进的基于条件熵的属性约简理论对8410份问卷调查数据进行计算与分析，得出与农民参保意愿密切相关的几个因素，如年龄、性别、收入、行业等。

Rough ideals been first introduced by Kuroki N. Under the condition of the congruence relation, a rough set of a subsemigroup was proved to be its subsemigroup, while that of a leftideal was also proved to be its leftideal.

Kuroki N研究了半群中的粗理想，首次提出了粗子半群和粗理想的概念，证明了在同余关系下，半群的粗糙集是半群，左理想的粗糙集是左理想。

In this paper, granularity in rough set theory is used to describe rules based on the concept of rough degree. A tool for knowledge dependent relation diagram is put forward. Then dimensional knowledge granularity decision space and the same construction mapping decision space based on multi-decision sorts is established, and decision algorithm based on KDRD and decision space is introduced. Finally, through combining with the technique of fault diagnosis, it is proved that the approach is very appropriate to the intelligent decision system.

本文利用粗糙集理论中的粗度概念，在对获取规则进行扩展性的粒度描述的基础上，提出了一种工具―知识依赖关系图，构造了n维知识粒度决策空间以及基于多决策类的同构映射决策空间，建立了基于KDRD和决策空间的决策算法，并结合机械故障诊断说明了本方法非常适于故障诊断等通过结论获取原因的智能决策系统。

As the extension of rough set,variable precision rough set reflects the difference of degrees of overlap of kinds and sets.

而可变精度粗糙集作为对经典粗糙集理论的扩展，体现了等价类与集合的重叠度程度上的差别。

After a brief summary on data mining and rough set theory, the research works in the thesis can be descript as follows: 1. To the problem that finding rules in enormous data is very timeconsumable and the expansibility of existed algorithms is not very good, the thesis proposes a new method to discompose large data table based on the concepts of positive region and the importance of attribute in rough set theory.

Rough集理论是一种新型的处理不确定性知识的数学工具，围绕着数据挖掘领域存在的问题，本文利用Rough集理论与Rough分析工具，提出若干解决方案，同时在具体处理问题过程中引入了信息理论、因子分析等方法，与Rough分析结合使用，讨论了Rough集技术在知识发现、关联规则挖掘、模式分类以及数据清洗等问题中的应用。

Then, we study Rough Set Theory approach to KDD, develop two algorithms for deriving Reduct and propose the integration of domain knowledge into Rough Set Theory to mining multiple-level rules.

本文在分析了传统的关联规则挖掘算法的基础上，给出了几个挖掘算法和一个递增修正算法；提出了模糊关联规则的概念及其挖掘算法；研究了结合背景知识的多层次关联规则的挖掘方法。

Liapunov—Schmidt method is one of the most important method in the bifurcation theory.

Liapunov—Schmidt方法是分叉理论的最重要方法之一。

Be courteous -- even when people are most discourteous to you .

要有礼貌──即使当別人对你最不礼貌的时候。