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- 模糊C均值算法FCM
- 模糊C-均值算法fuzzy C-means algorithm
- 符号模糊C均值算法FSCM
- 改进模糊c均值算法Improved Fuzzy c- Means Algorithm
- 模糊vague
- 快速模糊C-均值算法fast fuzzy c-means algorithm
- 模糊的hazy
- K均值算法K-means algorithm
- 均值算法Means algorithm
- 模糊c划分fuzzy c
- K-均值算法K-means algorithm
- 模糊C平均Fuzzy C-Mean(FCM)
- 模糊C中值法fuzzy C-meansmethod
- 帧叠靠前／帧叠靠后四等分均值算法FINAL-four equal-length overlapped forward /backward algorithm
- 模糊c-划分Fuzzy c-partitions
- 利用均值算法求解凸函数极小值的收敛性分析THE CONVERGENCE ANALYSIS OF CONVEX FUNCTION BY USING MEAN METHOD
- 模糊C聚类分析C-means clustering method
- 求拟凸函数本质极小的均值算法的收敛性分析Convergence Analysis of Mean Method for Solving Essential Minimum of Quasi-convex Function
- 修正的模糊C平均modified FCM( Fuzzy C-Means)
- 传统的K-均值算法选择的相似性度量通常是欧几里德距离的倒数，这种距离通常涉及所有的特征。The Euclidean distance is usually chosen as the similarity measure in the conventional K-means clustering algorithm, which usually relates to all attributes.
