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。
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。
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 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。
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。
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。
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。
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。
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。
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。
K-means algorithm is a classical clustering algorithm.
平均算法是经典的聚类算法。
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。
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。
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。
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。
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 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 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。
Experimental results show that the new algorithm for image segmentation accuracy than a single K means clustering algorithm and the ant colony clustering algorithm has greatly improved.
实验结果证明，新算法在图像分割处理的精确度上较单一的K均值和蚁群聚类算法有很大提高。
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。
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。
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