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.
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。
A hierarchic classification diagnosis model, based on fuzzy membership grade and rules is being proposed.
提出了一个基于模糊隶属度和规则的分类层次诊断模型。
Proposes a classification algorithm based on simplified fuzzy rules base combining fuzzy clustering with rough set.
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。
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.
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。
Secondly, an improved fuzzy association method was proposed to mine the classification association rules.
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；
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.
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。
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