先用 -均值聚类算法作粗糙划分，确定感兴趣类。
First we make a loose classification with k-means clustering algorithm to fix a category of interest.
该文提出了一种基于K近邻加权的混合C均值聚类算法。
A new weighted hybrid C-means clustering based on the K-nearest-neighbour rule is presented in this paper.
针对这个问题，很多稳健模糊C -均值聚类算法被提出。
Lots of robust fuzzy C-means algorithms have been proposed in the literature to solve this problem.
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 results revealed that fuzzy c-means clustering algorithm could be used to delineate management zones by using the given variables.
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 the characteristics of traffic flow, it USES fuzzy C-means clustering algorithm to deal with these fuzzy factors.
对于区域分割，使用基于加权平方欧式距离的均值聚类算法代替传统的均值聚类算法。
It applies weighted K-means clustering for region segmentation, instead of traditional K-means clustering.
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。
Based on the traditional fuzzy C-means clustering algorithm, a new fuzzy C-means clustering algorithm for interval data clustering is proposed.
采用模糊C—均值聚类算法对网络进行模糊化，利用改进的LMS算法对网络进行训练。
The FCNN is fuzzed by FCM algorithm and improved LMS algorithm is applied to tune the weight of FCNN.
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.
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. We design a new hybrid C-means clustering accordingly.
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.
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。
Without considering the spatial information of images, the original fuzzy C-means algorithm is very sensitive to image noise.
该算法基于图像的特点，利用K均值聚类算法将图像分成几个灰度区间，然后再分别进行均衡化。
According to the characters of the images, the algorithm separated image into several regions by K-means clustering algorithm, and each region is equalized respectively within their gray levels.
该算法基于图像的特点，利用K均值聚类算法将图像分成几个灰度区间，然后再分别进行均衡化。
The complexity of time and spatial is becoming the difficulty of K-Means clustering algorithm while it deals with the huge amounts of data sets.
基于多特征联合分布直方图理论和模糊c -均值聚类算法，我们提出了新的视频流模糊检索方法。
We bring out our video retrieval method based on multi-feature data association histogram and C-Mean fuzzy clustering algorithm.
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 classical C-means clustering algorithm (CMA) is a well-known clustering method to partition an image into homogeneous regions.
然后以K近邻规则为基础，计算出样本集的加权矩阵，最后得到基于K近邻加权的混合C均值聚类算法。
And then based on the K-nearest-neighbour rule, the weighted matrix of samples is computed. Lastly, weighted hybrid C-means clustering based on the K-nearest-neighbour rule is presented.
本文从几何角度给出模糊c均值聚类算法中隶属度的解释，这种解释能更好的说明模糊c均值聚类算法的本质。
An explanation of membership degree in FCM algorithm from geometry view is given, which is helpful to understand the essence of FCM algorithm.
针对K均值聚类算法依赖于初始值的选择，且容易收敛于局部极值的缺点，提出一种基于粒群优化的K均值算法。
Local optimality and initialization dependence disadvantages of K-means are analyzed and a PSO-based K-means algorithm is proposed.
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。
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.
均值聚类算法的执行时间过度依赖于初始点的选取，但是在实际问题中并不知道k的取值和怎样才能有效地选取初始点。
The running time of K-means overly depends on the initial points but the right value of k is unknown and selecting the initial points effectively is also difficult.
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.
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.
IRIS数据检验表明，未确知均值聚类算法误判样本数少、收敛速度快、鲁棒性好，是一种实用、有效的无监督聚类算法。
The data of IRIS indicates that the algorithm possesses the better convergence, better robustness and it is an unsupervised clustering algorithm.
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.
本文改进了传统FCM的目标函数，引入控制邻域作用紧密程度的参数，提出了一种能够更加合理地运用图像的空间信息，改进的模糊c -均值聚类算法。
Modifying the objective function of FCM and introducing a variable as the parameter to control the tight degree of neighborhood effect present a spatial model to FCM clustering algorithm.
本文改进了传统FCM的目标函数，引入控制邻域作用紧密程度的参数，提出了一种能够更加合理地运用图像的空间信息，改进的模糊c -均值聚类算法。
Modifying the objective function of FCM and introducing a variable as the parameter to control the tight degree of neighborhood effect present a spatial model to FCM clustering algorithm.
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