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.
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。
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.
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.
应用K均值自动聚类算法，提出了一种新的基于轨迹空间相似距离的轨迹分类算法，对以上获得的有效轨迹进行分类。
Using K Means which can automatically cluster trajectories, a new algorithm based on trajectory space similarity distance is presented, and it is applied to classify trajectory.
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.
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.
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.
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。
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.
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。
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.
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.
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。
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.
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.
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.
针对模糊c均值算法与粒子群算法的不足，提出了一种基于粒子群算法和模糊c—均值算法的混合聚类算法。
To avoid the shortcomings of FCM and Particle Swarm Optimization algorithm, new hybrid clustering algorithm based on PSO and FCM algorithm is proposed.
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.
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.
提出了一种k-均值聚类算法和SOM自组织神经网络算法相结合的异常检测模型，使得系统可以更好的分类正常数据流和异常数据流，以此来防范未知的攻击。
Secondly, the anomaly detection model based on K-means algorithm and SOM network is constructed. It can classify the normal and abnormal network data stream so better to detect the unknown attack.
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.
根据红外灰度图像的特点，提出了一种基于K -均值聚类的图像增强的新算法。
A new algorithm method of image enhancement based on K-means clustering is presented, according to the characteristics of infrared gray-image.
实验结果表明，基于自适应权重的粗糙K均值算法是一种较优的聚类算法。
The experiments indicate that the rough K-means based on self-adaptive weights is an effective rough clustering algorithm.
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.
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.
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