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。
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。
To improve the speed of image search, K-means Clustering is used to create the image database.
另外为提高了图像的检索速度，采用K均值聚类索引建立数据库。
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。
First we make a loose classification with k-means clustering algorithm to fix a category of interest.
先用 -均值聚类算法作粗糙划分，确定感兴趣类。
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。
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。
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。
The algorithm is then extended to use K-means clustering to seed the initial solution and the information pheromone is adjusted according to them.
对蚁群算法作了改进，思路是K-均值方法混合，利用K-均值方法的结果作为初值。
The Euclidean distance is usually chosen as the similarity measure in the conventional K-means clustering algorithm, which usually relates to all attributes.
传统的K-均值算法选择的相似性度量通常是欧几里德距离的倒数，这种距离通常涉及所有的特征。
A clustering segmentation algorithm based on an improved K-means clustering method is used to improve the efficiency and accuracy of 3d medical image segmentation.
为提高三维医学数据场的分割效率和准确率，本文利用特征聚类技术，提出了一种新的基于改进K - means聚类的三维医学数据场的体分割算法。
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。
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。
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。
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。
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。
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。
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。
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, 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。
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。
K-means algorithm is a classical clustering algorithm.
平均算法是经典的聚类算法。
Clustering algorithms are the typical algorithms in the data mining, the K-means algorithm is the most basic algorithm, which has produced many classics and highly effective algorithms.
聚类是数据挖掘中的典型算法，其中的K -均值算法是最基本的算法，由该算法产生了许多经典而高效的算法。
After analyzing the traditional clustering algorithms, the paper presents a new clustering ensemble method based on K-means to cluster data.
本文在分析传统聚类算法的基础上，提出了一种聚类融合算法。
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。
Researched the unsupervised anomaly detection methods based on clustering analysis, improved the K-means algorithm.
研究了基于聚类分析的非监督式异常检测方法，并改进了K均值算法用于聚类分析。
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。
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。
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