该算法以属性重要度为迭代准则得到属性集合的最小约简。
This algorithm takes the importance of attribute as the iterative criterion and finds the least reduction of attribute-set.
保持依赖度不变的最小条件属性集为条件属性的最小约简集。
The minimum condition attribute set which keeps the dependency degree changeless is just the minimum reducing set of condition attribute set.
人们总期望找到最小约简，但这已被证明是一个NP完全问题。
People always expect to find out minimum reduction, but it has already been proved that it is a NP complete problem.
本文提出图表示下的知识约简，给出图表示下求最小约简的完备递归算法。
A complete recursive algorithm for minimal reduction under graph view is designed.
该模型利用粗糙集中知识白勺信息量对属性停止约简，并产生出最小约简。
This model reduces attributes by knowledge amount of information for a rough set, and a minimal reduction set is formed.
对分辨矩阵求核过程进行改进与扩展，给出了一种以属性频度作为启发式信息计算最小约简快速完备方法。
Through improving and extending the process of computing core, the minimal reduction algorithm based on attribute frequency heuristic information are put forward.
实验证明该算法是有效的，并能求解出信息系统中多组不同的最小约简，为决策支持和数据挖掘等提供更多信息。
Experimental results show the algorithm is effective. It can find different reductions of attribute in the information system and provide more information for decision support and data mining.
研究表明，最小约简的计算和全部约简的求算都是NP问题，在人工智能中，解决这类问题的一般方法是利用启发式信息进行约简。
It has been proved the computation of minimal reduction and full reduction both is NP-hard problem, in artificial intelligence the common way is to employ heuristic knowledge to reduce.
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.
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.
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.
通过实例说明，该算法能得到不完备决策表的最小相对约简。
An example shows this algorithm can achieve the minimal relative reduction of incomplete decision table.
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.
以原始条件属性集为起点并结合算子，通过向属性核的递减式逼近，得到属性的最小相对约简。
Acquiring optimal relative reduction by descending approach to core of attribute from original set of conditional attribute and combining with operator.
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.
利用该方法能准确地求出信息系统中所有的最小子集，且计算量少于由定义来约简。
With this method, all minimal subset can be resolved and the computational complexity is less than solution from definition.
极大相容块是非完备信息系统中的最小知识单元，在非完备信息系统的知识表示、属性约简、粒度分析及知识获取方面有重要的应用价值。
As minimum knowledge units, maximal consistent blocks are very useful for knowledge representation, attribute reduction, granular analysis and knowledge acquisition in incomplete information systems.
同样，基于粗糙集理论对特征集进行约简，在最优决策属性的基础上使用最小二乘支持向量机分类器对流型进行识别。
Similarly, based on rough set theory to feature-set reduction, in the optimal decision based on the use of the property least squares support vector machine classifier to identify the flow pattern.
首先介绍了决策表、广义信息表的构造及特点，然后给出了求决策表的最小属性约简及最小决策算法的计算方法。
Then, according to the features of the generalized information table, the algorithms of acquiring minimal attribute reduction, attribute value reduction and minimum decision algorithm are put forward.
实例分析证明，该算法能节省计算时间，求出最小属性约简。
Example analysis shows that the time is reduced, and it illustrates the minimum attribute reduction of the new algorithm.
实例分析证明，该算法能节省计算时间，求出最小属性约简。
Example analysis shows that the time is reduced, and it illustrates the minimum attribute reduction of the new algorithm.
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