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。
A general solution is to add the spatial information to the object function of fuzzy C-means.
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。
Lots of robust fuzzy C-means algorithms have been proposed in the literature to solve this problem.
针对这个问题，很多稳健模糊C -均值聚类算法被提出。
Then use Fuzzy C-means to do document clustering based on the results of similarity calculation above.
然后采用模糊c均值根据上述计算文档相似度的结果对文档进行聚类。
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。
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。
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。
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。
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。
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。
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。
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。
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。
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。
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。
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。
To improve the accuracy of text clustering, fuzzy c-means clustering based on topic concept sub-space (TCS2FCM) is introduced for classifying texts.
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。
Aiming at the characteristic of recognizing grain pest, a method is proposed with fuzzy theory. Fuzzy C-means clustering is introduced and remarked firstly.
针对谷物害虫图像识别的特点，提出了基于模糊理论的害虫图像识别方法。
Considering fuzzy C-means clustering algorithms are sensitive to initialization and easy fall - en to local minimum, a novel optimization method is proposed.
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。
Fuzzy C-means clustering is one of the important learning algorithms in the field of pattern recognition, which has been applied early to image segmentation.
模糊c -均值聚类是模式识别中的重要算法之一，很早就被应用到图像分割中。
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 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.
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。
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。
Aimed at the disadvantages of fuzzy C-means in fault diagnosis of steam turbine set, a weighted fuzzy clustering method based on particle swarm optimization is put forward.
针对模糊c -均值算法在汽轮机故障诊断中的不足，提出了粒子群优化加权模糊聚类分析的方法。
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。
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)。
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)。
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