通过理论分析，属性均值聚类是比模糊均值聚类更稳健的聚类方法。
Attribute means clustering is more robust than fuzzy means clustering by theoretical analysis and numerical example.
这里提出了一种高效的基于模糊c均值(FCM)聚类的彩色图像分割方法，它利用塔形数据结构对彩色图像进行多层分割。
An efficient segmentation method based upon fuzzy c-means (FCM) clustering principles is proposed. The approach utilizes a pyramid data structure for the hierarchical ana - lysis of color images.
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。
A new method integrated with fluctuation method and fuzzy C-means clustering was put forward and solved the above difficult problems.
结果模糊K- 均值聚类算法能很好地分割出磁共振颅脑图像中的灰质、 白质和脑脊液。
Results Fuzzy K means clustering algorithm can segment white matter, gray matter and CSF better from the MR head images.
针对图像混合噪声提出了一种新型的模糊加权均值滤波算法。
A new efficient fuzzy weighted mean filter approach to the restoration of images corrupted by mixed noise was proposed.
该文提出了一种将模糊C -均值聚类法的各种改进算法与矢量量化法相结合的说话人辨认的新方法。
Several new algorithms of fuzzy C-mean clustering with the combination of vector quantization are proposed for speaker identification.
通过对模糊c均值算法聚类特性的分析，引入了约束函数及模式相似度的概念，提出了改进的FCM算法。
With the clustering feature analyzed, restrained function and pattern similarity are introduced. Then the algorithm of improved FCM is presented.
同时，在模糊C-均值聚类基础上选择训练样本比起直接基于真实地物图选择，减少了主观因素对训练样本选择的影响，因此取得了更高的分类精度。
Selecting train sample on the basis of fuzzy C-mean clustering decreased subjective factor affecting selecting train sample, so higher classification accuracy can be achieved.
取后验均值作为模糊点估计值后，便得到损失频度与强度分布。
After posterior mean is exploited as fuzzy point estimate, loss frequency distribution and severity distribution are gotten.
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。
The improved fuzzy C-means clustering algorithm has better robustness and makes the cluster results insensitive to the predefined cluster number.
该文在子镜头的关键帧提取方法基础上，利用模糊c -均值聚类算法，实现了一种基于子镜头聚类的情节代表帧选取方法。
An algorithm for selecting episode representation frames by using an approach of key frame extraction based on multiple characters and C-Mean fuzzy clustering is detailed in the paper.
分别采用模糊c -均值聚类方法和快速全局C -均值聚类两种算法实现化工建模所需训练数据的有效提取。
In order to getting the effective training data of chemical engineering modeling, two algorithms that fuzzy C-means and fast global fuzzy C-means clustering were used.
首先用阈值分割法去除红毛丹背景，然后用模糊C均值聚类方法来分割果肉区域。
The rambutan flesh was segmented using the FCM (fuzzy C-mean) clustering method after removing the background of the image.
模糊模型的前件和后件参数分别采用模糊C均值聚类（FCM）和正交最小二乘法（OLS）进行离线或在线辨识。
The T-S fuzzy model's parameters are identified by methods of fuzzy C mean(FCM) and orthogonal least-squares(OLS) online or otherwise.
为了改善文本聚类的准确度，提出用基于主题概念子空间的模糊c -均值聚类(TCS2FCM)方法来分类文本。
To improve the accuracy of text clustering, fuzzy c-means clustering based on topic concept sub-space (TCS2FCM) is introduced for classifying texts.
在模糊c -均值聚类的基础上选择训练样本，可以提高训练样本的准确度，满足了训练样本所需的单一性原则。
Selecting train sample on the basis of fuzzy C-mean clustering can improve accuracy of train sample, singleness of train samples can be satisfied.
提出了基于模糊C均值聚类和图像匹配，检测喷雾锥角和喷雾不均匀度的方法，并应用于发动机喷嘴性能检测。
A method based on fuzzy C-mean clustering and image matching algorithms are proposed to detect atomization Angle and uniformity, applied to performance test-bed of engine nozzle.
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。
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.
一个假设可信度值CFV的最终值是根据各可信度值的平均值和模糊熵来确定的。
The final CFV for hypothesis is given based on mean and fuzzy entropy of all CFV.
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)。
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.
模糊c均值算法(FCM)是经常使用的聚类算法之一。
The fuzzy c-means algorithm (FCM) is one of widely used clustering algorithms.
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。
This paper discusses the fuzzy C-means algorithm (FCM), one of the fuzzy clustering methods and clustering validity measurements.
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。
Firstly, the advantages of fuzzy C-means clustering and possibilistic C-means clustering are utilized in this paper.
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。
A clustering algorithm for Chinese documents based on the spherical fuzzy c-means algorithm is presented.
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。
An improved color segmentation algorithm is presented based on weighting fuzzy c-means (FCM) clustering algorithm.
通常的做法是在原来模糊c -均值聚类的目标函数中加入空间信息惩罚项。
A general solution is to add the spatial information to the object function of fuzzy C-means.
针对模糊C均值聚类算法对初始值敏感、易陷入局部最优的缺陷，提出一种新的优化方法。
Considering fuzzy C-means clustering algorithms are sensitive to initialization and easy fall - en to local minimum, a novel optimization method is proposed.
论文采用了一种基于改进的模糊C均值算法来聚类图像。
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。
This paper proposes a modified fuzzy C-means (MFCM) clustering algorithm to cluster all images before retrieval.
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