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.
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.
提出了一个基于模糊隶属度和规则的分类层次诊断模型。
A hierarchic classification diagnosis model, based on fuzzy membership grade and rules is being proposed.
用这样一个模糊规则来表示分类的模糊系统，更加有效地构建了一个能对训练样本比较准确分类的模糊分类器。
This fuzzy system design method that uses a fuzzy rule to represent a cluster is then propsed so that a fuzzy classifier can be efficiently constructed to correctly classify the training data.
基于竞争学习算法的模糊分类器确定系统的模糊空间和模糊规则数，并得出每个样本对每条规则的适用程度。
The fuzzy space structure of system and the number of fuzzy rules based on fuzzy competitive learning algorithm are determined and the fitness degree of each rule contrast to each sample is obtained.
然后，在此基础上提出一种改进的模糊关联算法挖掘分类关联规则；
Secondly, an improved fuzzy association method was proposed to mine the classification association rules.
提出一种从训练样本提取基于超盒表示的模糊规则的方法，用于模式分类。
In this paper, we discuss a new method for rule extraction based on hyper-box representation.
然后，通过设计的模糊推理规则进行模式的分类。
Then patterns are categorized by the designed fuzzy inference regulation.
结合模糊聚类和粗糙集提出了一种基于精简的模糊规则库分类算法。
Proposes a classification algorithm based on simplified fuzzy rules base combining fuzzy clustering with rough set.
但是各类作品分类标准模糊、要件选择随意以及权利分配原则矛盾、分配规则粗疏，导致弊病丛生。
Howev- er, there are a lot of question in the provisions such as vague classification standards, random selection of elements of the concept, contradictory and simple distribution rights.
从实验结果可以看出将两者结合设计出的模糊分类器具有分类准确率高、模糊描述简单、规则少且易于理解等特点。
The experimental results show that the proposed fuzzy classifier based on AFS theory and Genetic Algorithm has few rules, high classification rate, and good interpretability.
然后，通过设计的模糊推理规则进行模式的分类，这种分段线性分类器的设计提高了算法线性分类的能力。
Then patterns are categorized by the designed fuzzy inference regulation. The design of this piecewise linear classifier enhances the ability of linear classification of the algorithm.
而基于模糊规则的模式识别方法是一类可理解性好的非线性方法，但迄今为止还没有被应用于多分类器融合问题中。
As a nonlinear method, the fuzzy rule-based pattern recognition has good comprehensibility, but has not been applied to the multiple classifier fusion.
对于具有多模糊特征变量的多分类问题，自动提取适当的模糊模式识别规则集至关重要。
It's important to extract an appropriate fuzzy rules set for multi-classification problems that have fuzzy variables.
对于具有多模糊特征变量的多分类问题，自动提取适当的模糊模式识别规则集至关重要。
It's important to extract an appropriate fuzzy rules set for multi-classification problems that have fuzzy variables.
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