模糊C均值聚类(fuzzy C-means clustering,FCM)是一种经典的基于灰度的非监督聚类算法[9-10],能有效分析图像的灰度特征,也是图像分割的一种重要方...
基于26个网页-相关网页
对于属性模糊化，针对基于模糊C均值聚类(fuzzy c-means clustering: FCM)的模糊化方法易受孤立点影响的问题，提出了一种基于改进FCM的模糊化方法，实验表明该方法可以获得更准确的模糊化结果。
基于12个网页-相关网页
Fuzzy C-Means Clustering Algorithm 模糊C均值聚类算法 ; 模糊C ; 均值聚类方法 ; 法
fast fuzzy c-means clustering 快速模糊c均值聚类
Weighted fuzzy C-means clustering 加权模糊C
improved fuzzy C-means clustering 改进的模糊C
Fuzzy c-means clustering method 均值聚类
Kernel-Based Fuzzy C-Means Clustering Arithmetic 基于核函数模糊C均值聚类
fuzzy c-means clustering fcm 模糊c
Firstly, the advantages of fuzzy C-means clustering and possibilistic C-means clustering are utilized in this paper.
首先该文利用模糊C均值聚类和可能性C均值聚类的优点，设计出一种混合C均值聚类算法。
According to the characteristics of traffic flow, it USES fuzzy C-means clustering algorithm to deal with these fuzzy factors.
根据交通流特性，运用模糊C均值聚类算法对交通流各要素进行模糊分析处理。
A new method integrated with fluctuation method and fuzzy C-means clustering was put forward and solved the above difficult problems.
文中提出的波动法与模糊c -均值聚类相结合的状态评级则有效地解决了上述问题。
应用推荐