The fuzzy c-means algorithm (FCM) is one of widely used clustering algorithms.
模糊c均值算法(FCM)是经常使用的聚类算法之一。
The C-means algorithm is treated as a new search operator in order to improve the convergence speed.
算法还集成了一种C -均值搜索算子，用于加快收敛速度。
A clustering algorithm for Chinese documents based on the spherical fuzzy c-means algorithm is presented.
提出一种基于球形的模糊c -均值算法的中文文本聚类方法。
Without considering the spatial information of images, the original fuzzy C-means algorithm is very sensitive to image noise.
由于原始的模糊c -均值聚类算法没有考虑图像的空间信息，算法对图像中的噪音点十分敏感。
This paper discusses the fuzzy C-means algorithm (FCM), one of the fuzzy clustering methods and clustering validity measurements.
本文讨论了模糊聚类中的模糊C均值算法和聚类有效性测度。
Based on fuzzy C-Means algorithm (FCM) and fuzzy Min-Max Neural Networks, an integrated algorithm for fuzzy pattern recognition using hypercube set was proposed.
结合模糊c均值算法(FCM)与模糊最小最大神经网络算法，提出一种基于超长方体集的模糊模式识别算法。
A dot density weighted fuzzy C-means algorithm is proposed by using density size of data dot regarded as weighted value and distributing characteristic of datas own.
利用数据点的密度大小作为权值，借助数据本身的分布特性，提出了一种点密度加权模糊c -均值算法。
In this article we combine the fuzzy C-means algorithm with fuzzy measures and fuzzy integrals and apply the two algorithms to the medicinal pathological image segmentation.
本文将经典的模糊c -均值聚类算法和模糊测度和模糊积分结合起来，并将这两种算法应用于医学病理图象的分割。
Inspired by the clone selection principle and memory mechanism of the vertebrate immune system, a hybrid algorithm combining C-means algorithm and artificial immune algorithm is presented.
通过借鉴生物免疫系统中的克隆选择原理和记忆机制，提出了一种人工免疫c -均值混合聚类算法。
An improved color segmentation algorithm is presented based on weighting fuzzy c-means (FCM) clustering algorithm.
在加权模糊c -均值(FCM)聚类算法的基础上，对分色算法进行了改进。
It is a procedure of the label following an unsupervised fuzzy clustering that fuzzy c-means (FCM) algorithm is applied to image segmentation.
算法用于图像分割是一种非监督模糊聚类后再标定的过程。
The classical C-means clustering algorithm (CMA) is a well-known clustering method to partition an image into homogeneous regions.
经典的C -均值聚类算法（CMA）是将图像分割成C类的常用方法，但依赖于初始聚类中心的选择。
The traditional fuzzy C-means (FCM) algorithm is an optimization algorithm based on gradient descending. it is sensitive to the initial condition and liable to be trapped in a local minimum.
传统的模糊c -均值(FCM)聚类是一种基于梯度下降的优化算法，该方法对初始化较敏感，且易陷入局部极小。
In the paper, a suppressed fuzzy c-means (S-FCM) algorithm, for intensity image segmentation, is proposed on the basis of the characters of FCM algorithm and intensity images.
该文根据FCM算法和灰度图像的特点，提出了一种适用于灰度图像分割的抑制式模糊C -均值聚类算法(S - FCM)。
This paper proposes a modified fuzzy C-means (MFCM) clustering algorithm to cluster all images before retrieval.
论文采用了一种基于改进的模糊C均值算法来聚类图像。
The results revealed that fuzzy c-means clustering algorithm could be used to delineate management zones by using the given variables.
利用所选取的变量，模糊c均值聚类算法可以较好地进行管理分区划分。
The improved fuzzy C-means clustering algorithm has better robustness and makes the cluster results insensitive to the predefined cluster number.
改进后的模糊C-均值聚类算法具有更好的鲁棒性，且放松了隶属度条件，使得最终聚类结果对预先确定的聚类数目不敏感。
Based on the traditional fuzzy C-means clustering algorithm, a new fuzzy C-means clustering algorithm for interval data clustering is proposed.
在传统模糊c -均值聚类算法的基础上，提出了一种新型区间值数据模糊聚类算法。
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。
According to signals sparsity by Curvelet transform, the mixed matrix can be estimated with C-means cluster analysis, and the estimated value is looked as initial value of BSS algorithm.
该方法利用Curvelet多尺度几何分析后信号的稀疏性特点，采用了C - means聚类方法寻求混合矩阵估计值，把该估计值作为算法初始值。
According to signals sparsity by Curvelet transform, the mixed matrix can be estimated with C-means cluster analysis, and the estimated value is looked as initial value of BSS algorithm.
该方法利用Curvelet多尺度几何分析后信号的稀疏性特点，采用了C - means聚类方法寻求混合矩阵估计值，把该估计值作为算法初始值。
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