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。
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。
Local optimality and initialization dependence disadvantages of K-means are analyzed and a PSO-based K-means algorithm is proposed.
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。
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。
This article analyzes the deficiency of K-means algorithm and improves the algorithm with relative best partition and weight in the computation of distance of clusters and cases.
针对K -平均算法存在的缺陷，通过引入相对最佳随机划分方法以及在计算样本与簇中心时的权重，改进了K -平均算法。
The running time of K-means overly depends on the initial points but the right value of k is unknown and selecting the initial points effectively is also difficult.
均值聚类算法的执行时间过度依赖于初始点的选取，但是在实际问题中并不知道k的取值和怎样才能有效地选取初始点。
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。
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.
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。
And then using Boolean equations containing gate variables and means of OBDD, an efficient algorithm for computing the K-terminal reliability of a network is also proposed.
然后，基于具有门限变量的布尔方程和有序二分决策图方法(OBDD)，给出计算k -终端网络可靠度算法。
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。
This paper USES the improved K-means (IKM) algorithm to process the missing data and thus improve the precision of the Naive Bayes classifier.
本文利用改进的K -均值算法对缺失数据进行处理，提高了朴素贝叶斯分类的精确度。
K-means algorithm is a classical clustering algorithm.
平均算法是经典的聚类算法。
However, owing to random selection of initial centers, unstable results were often obtained while using traditional K-means and its variants.
然而，由于聚类初始中心点选择的随机性，传统K -均值算法以及其变种的聚类结果会产生较大的波动。
Traditional K-Means algorithm is sensitive to the initial centers and easy to get stuck at locally optimal value.
传统K均值算法对初始聚类中心敏感，聚类结果随不同的初始输入而波动，容易陷入局部最优值。
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。
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。
K-means algorithm has some deficiencies. The number K must be pointed and its effectiveness liable to be effected by isolated data and the input sequence of data.
均值算法的聚类个数k需指定，聚类结果与数据输入顺序相关，而且易受孤立点影响。
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。
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。
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。
The experimental result shows that the K-means with the proposed technique can produce cluster results with high purity as well as good stableness.
实验表明，该算法能够生成质量较高而且波动性较小的聚类结果。
In cluster analysis, Fuzzy K-Means (FKM) algorithm is one of the most widely used methods. However, FKM algorithm is much more sensitive to the initialization, and easy to fall into local optimum.
在聚类分析中，模糊k均值算法是目前应用最为广泛的方法之一，然而该算法对初始化敏感，容易陷入局部极值点。
Although the traditional K - means algorithm has good convergence rate and can be realized easily, it can easily be trapped in a local optimum, and it is sensitive in initial setting.
传统的K -均值方法用于聚类具有收敛速度快、算法实现简单等特点，但容易陷入局部最优，并对初始解敏感。
Although the traditional K - means algorithm has good convergence rate and can be realized easily, it can easily be trapped in a local optimum, and it is sensitive in initial setting.
传统的K -均值方法用于聚类具有收敛速度快、算法实现简单等特点，但容易陷入局部最优，并对初始解敏感。
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