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.
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。
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。
A new non-Euclidean distance was introduced to replace the Euclidean distance in the IPCM, and then a new fuzzy clustering, called novel improved possibilistic C-means (NIPCM) clustering was proposed.
通过引入一种新的非欧式距离以替代IPCM目标函数中的欧式距离，提出了一种称为新的改进型可能C -均值聚类(NIPCM)算法。
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。
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。
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。
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。
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。
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。
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。
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。
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。
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。
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。
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。
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