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1)  variable fuzzy clustering
可变模糊聚类
1.
Rationality analysis and application test of variable fuzzy clustering iterative model
可变模糊聚类迭代模型合理性分析与应用检验
2.
In order to avoid defects of classical fuzzy clustering iterative model,variable fuzzy clustering iterative model(VFCIM) based on variable fuzzy set theory is introduced.
针对经典模糊聚类迭代模型中不考虑聚类指标权重的缺点,基于可变模糊集理论给出了考虑模型参数指标权重变化的可变模糊聚类迭代模型。
2)  Possibility Fuzzy Clustering
可能模糊聚类
1.
Possibility Fuzzy Clustering Approach with Weighted Features;
一种基于加权特征的可能模糊聚类方法
3)  fuzzy clustering
模糊聚类
1.
The application of fuzzy clustering analysis in the comfort evaluation of garment;
模糊聚类分析在服装舒适性评价上的应用
2.
Recognition of the key economic factors of petroleum industry equipment management based on fuzzy clustering;
基于模糊聚类的石油工业设备管理关键经济要素识别
3.
Optimized formation assignment for large-scale air fleet using fuzzy clustering and genetic algorithm;
基于遗传模糊聚类的机群编队最优分配方法
4)  fuzzy cluster
模糊聚类
1.
A Fuzzy Cluster Analysis on Choice of Alternatives in Steel Enterprise Cooperation Innovation;
钢铁企业合作创新项目选择的模糊聚类分析
2.
Application of fuzzy cluster analysis in determining fingerprint spectrum of curcuma aromatica salib by pyrolysis gas chromatography;
模糊聚类分析法在郁金裂解色谱测定指纹图谱中的应用研究
3.
Application of fuzzy cluster analysis to water quality diagnosis pattern in circulating cooling water system;
模糊聚类应用于循环冷却水系统的水质诊断模式
5)  fuzzy classification
模糊聚类
1.
The fuzzy classification analysis of engineering bidding price;
工程标底的模糊聚类分析
2.
Drought analysis based on stochastic simulation and fuzzy classification;
基于随机模拟和模糊聚类的水文干旱特性分析
3.
A new method of diagnosing in\|cylinder combustion deficiency based on fuzzy classification theory;
基于模糊聚类分析识别内燃机缸内燃烧异常的故障
6)  fuzzy cluster analysis
模糊聚类
1.
Application of Fuzzy Cluster Analysis on Classification of Cultivated Land;
模糊聚类分析在耕地分类中的应用
2.
Customer Demands Identify and Demands Identification s Fuzzy Cluster Analysis Methods;
顾客需求识别及模糊聚类识别方法
3.
Quantitative evaluation of complex carbonate reservoir based on fuzzy cluster analysis
基于模糊聚类分析的复杂碳酸盐岩储层定量评价
补充资料:动态模糊聚类法
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性质:又称动态模糊聚类法。选定一批聚类中心,其指标能反映该类的特征,将样本向最近的聚类中心聚类。再根据分类结果确定新的聚类中心,其各项指标为该类中所有样本的相应指标的平均值。然后计算前后两聚类中心的差异,如差异大于某一阈值,说明分类不合理,需修改分类,即以新的聚类中心代替旧的聚类中心,直到前后两聚类中心的差异小于某一阈值,认为分类合理,从而终止分类过程。

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