Common approaches to unsupervised learning include k-Means, hierarchical clustering, and self-organizing maps.
无监管学习的常见方法包括k - Means、分层集群和自组织地图。
This is supported by the comparison with the results of hierarchical clustering segmentation of point cloud model and K-Means clustering segmentation of mesh model.
与三维网格模型的K均值聚类分割、点云模型的谱系聚类分割的实验结果比较证实了这一点。
Mahout provides driver programs for all of the clustering algorithms, including the k-Means algorithm, aptly named the KMeansDriver.
Mahout为所有集群算法都提供了驱动程序，包括k - Means算法，更合适的名称应该是KMeansDriver。
Popular approaches include k-Means and hierarchical clustering.
流行的方法包括k - Means和分层集群。
Given a set of vectors, the next step is to run the k-Means clustering algorithm.
创建了一组矢量之后，接下来需要运行k - Means集群算法。
Research of K value optimization of spatial clustering.
典型空间聚类问题的k值优化研究。
This paper introduces an intrusion detection model based on clustering analysis and realizes an algorithm of K-means which can set up a database of intrusion detection and classify safe levels.
提出了一种基于聚类分析方法构建入侵检测库的模型，实现了按k -平均值方法建立入侵检测库并据此划分安全等级的思想。
The clustering method based on partitioning is mainly included K-Means and K-Medoids; the other methods are the mutation of these two methods.
基于划分的聚类算法主要有K均值和K中心点算法，其他的方法都是这两种算法的变种。
Results Fuzzy K means clustering algorithm can segment white matter, gray matter and CSF better from the MR head images.
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。
Firstly, nucleus regions of leukocytes in images are automatically segmented by K-mean clustering method. Then single leukocyte region is detected by utilizing thresholding algorithm segmentation.
首先，运用K-均值聚类方法提取出细胞核，并且采用多域值分割演算法去除细胞图像中的背景区域。
The function model of the multiquadric's central node to choose to do an in-depth discussion, put forward the "Adaptive location" to match the characteristics of the K-means clustering method.
对函数模型法中的多面函数中心节点的选择做了深入讨论，提出了具有“位置自适应”匹配特点的K均值聚类法。
K-means algorithm is a classical clustering algorithm.
平均算法是经典的聚类算法。
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。
This paper proposed a novel hybrid algorithm for clustering analysis based on artificial fish-school algorithm and K-means.
结合人工鱼群算法的全局寻优优点提出了一种基于人工鱼群算法的K -平均混合聚类分析算法。
A new weighted hybrid C-means clustering based on the K-nearest-neighbour rule is presented in this paper.
该文提出了一种基于K近邻加权的混合C均值聚类算法。
By means of analyzing kernel clustering algorithm and rough set theory, a novel clustering algorithm, rough kernel k-means clustering algorithm, was proposed for clustering analysis.
通过研究核聚类算法，以及粗糙集，提出了一个新的用于聚类分析的粗糙核聚类方法。
The experiments indicate that the rough K-means based on self-adaptive weights is an effective rough clustering algorithm.
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 complexity of time and spatial is becoming the difficulty of K-Means clustering algorithm while it deals with the huge amounts of data sets.
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。
Firstly, on bisecting K-means is used to quantize image roughly and then we refine the image by improved spectral clustering based weighted distance.
首先利用高效的二分K均值聚类进行粗略量化，然后使用基于加权距离的谱聚类进行再次量化。
Firstly, RAC and K-means clustering method are combined in this algorithm by the way of searching pre-matches feature points, which are called the cluster point set, of the unknown model.
此算法首先结合RAC和K -均值聚类方法对未知模型的特征点进行预匹配，得到的匹配结果称为聚类点集。
And this paper also improved the initial center point's selection of K-Means clustering algorithm.
另对聚类算法初始聚类中心的选取也做了改进。
It applies weighted K-means clustering for region segmentation, instead of traditional K-means clustering.
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。
On the basis of balancing algorithm efficiency and accuracy of clustering, improved formula of clustering distance and K average algorithm approach were presented. Satisfactory results were achieved.
并且在权衡算法效率和聚类精度的基础之上提出了改进的聚类距离公式和K -平均算法，达到了较好效果。
A new algorithm method of image enhancement based on K-means clustering is presented, according to the characteristics of infrared gray-image.
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。
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.
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。
After that, unsupervised K-means clustering was calculated to complete spike sorting.
最后通过非监督的K均值方法完成动作电位聚类。
The experimental results show that the proposed algorithm is superior to the improved kernel clustering algorithm and K-means in good astringency, stability and overall optimal solutions.
实验结果表明，使用该算法的聚类比改进的核聚算法、K均值算法等单一方法具有良好的收敛性、稳定性和更高的全局最优。
The initial clustering center of the traditional K-means algorithm was generated randomly from the data set, and the clustering result was unstable.
传统的K均值算法的初始聚类中心从数据集中随机产生，聚类结果很不稳定。
The initial clustering center of the traditional K-means algorithm was generated randomly from the data set, and the clustering result was unstable.
传统的K均值算法的初始聚类中心从数据集中随机产生，聚类结果很不稳定。
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