提出了一种新型的决策规则约简方法。
A method of reduction of decision rulers is proposed based on rough set and fuzzy set.
结果表明，该算法能够正确地对实值属性信息系统进行规则约简。
The experimental results show that the algorithm can correctly reduce rules for real value attribute information system.
进一步地，论文发现了现有一个规则约简算法中存在的错误，提出了一种新的规则约简算法，从而获得小且易于合并的简化规则集。
This new method extracts rule which has the least condition or contains the most core-value in the rule core-set. We can gain shortest and uniteable rules based on the former selecting policy.
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.
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.
最后应用粗糙集理论来对决策信息表进行离散化处理和属性约简，以生成图像分割算法选取的决策规则。
Finally, rough set theory is applied to discretization and attribution reduction of decision information table, in order to make the decision rule of image segmentation algorithm selection.
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.
一方面分析了动态自主知识获取问题中的决策表动态约简问题，确定了在获得基本最小规则集后动态增加或减少规则的算法。
Dynamic reduction problem is discussed and algorithm adding and reducing rules in the decision-making table dynamically are put foreword so as to reduce the computing complexity.
最后，建立了一个利用约简决策表的距离图求决策规则的核值及最小决策算法的算法框架。
At last, applying the distance graph of the reduced decision table, we propose a way to get the core of each decision rule.
该文分析属性值约简，针对协调决策表提出一种通过构造决策矩阵直接获取最简规则的方法。
This paper analyses attribute value reduction, and presents a method to acquire briefest rules directly by constructing decision matrix in consistent decision table.
因此，有必要对镜头边界检测的规则进行特征约简。
Therefore, it is necessary to perform feature reduction for the decision rules of shot boundary.
改进了一种粗糙集决策表的值约简算法，并将其应用到文本分类规则的提取中。
A reduction algorithm based on rough set is improved and then applicated to extract the rules of text categorization.
然后在属性值约简中进一步去除与用户无关的属性，从而抽取个性化决策规则。
Secondly, during reduction of attribute values, some irrelevant attributes are further eliminated, then abstractation of personalized decision rules is accomplished.
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.
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 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.
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.
粗糙集中决策表约简也就是以基于最少的条件属性和最小冗余的属性值导出最少的决策规则或分类规则。
Simplification of Decision tables in Roouh set is order to lead decision rule or categorised rule at the least on the basis of the least condition attribute and minimum redundant attribute value.
对矩阵进行化简得到属性约简并生成规则。
但是属性约简是一个NP问题，对属性的约简和决策规则的约简只能通过启发式算法实现。
But the attribute reduction is a NP problem, the attribution reduction and decision rule reduction will be solved by method of elicitation.
为了获得简明的规则集，通常希望能找出最小的属性约简集，而求解最小约简是NP难问题，解决此类难题通常采用启发式算法以求得近似最优解。
The minimum attributes reduction set is expected to acquire the brief regulated set. This is taken as NP-hard Problem, which can be figured out through the heuristic algorithm.
同时，借助决策矩阵进行值约简，提出了一种新的规则提取算法，使最终得到的决策规则更加简洁。
Furthermore, a new algorithm for rule extraction based on decision matrices was presented. And much more concise decision rules could be got with this method.
知识约简是其中的核心内容，是在保持分类能力基本不变的情况下，获得系统的约简属性和分类规则。
Knowledge reduction is the core of Rough set. It obtains the reduction attribute and classification rules while holding the ability of classification unchanged.
本文中用户行为特征提取算法的设计主要借鉴了粗糙集理论中的属性约简与规则提取的思想。
The user feature extraction algorithmic in this paper reference from attribute reduction and decision rule extraction of rough set theory.
基于粗糙集方法提出了一种系统的决策表约简和决策规则提取方法。
Based on the rough set theory, a new systematic method is proposed to reduce the decision table and induce the decision-making rules.
新的算法对生成的带频度属性的决策规则进行约简处理，得出最简决策规则。
The new algorithm carries out the reduction processing to the generated decision rules containing the frequency attribute and obtains the simplest decision rules.
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.
以粗糙逻辑为基础，首先给出了在新实例加入论域后判断约简变化与否以及判断原极小决策算法中决策规则变化与否的判定依据。
Based on rough logic, theorems is presented, whether attribute reduction and minimal decision algorithm change or not when a new instance is added to the universe.
新的约简模型将满足平均决策强度条件的最简规则集作为最终的约简结果，解决了不相容决策表约简结果不一致的问题。
The most concise decision rule set that satisfies condition of mean decision power is regarded as final reduction result in the new reduction model.
通过属性约简和值约简，找出影响成型质量的关键因素，从而得到判断成型结果的决策规则。
The key factors affecting the quality of molding were detected through attribute reduction and value reduction, and the decision rules important to control the molding results were thus obtained.
通过属性约简和值约简，找出影响成型质量的关键因素，从而得到判断成型结果的决策规则。
The key factors affecting the quality of molding were detected through attribute reduction and value reduction, and the decision rules important to control the molding results were thus obtained.
应用推荐