K-means algorithm is a classical clustering algorithm.
平均算法是经典的聚类算法。
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。
There are lots of drawbacks to traditional incremental K-means in event detection.
传统的增量k均值法用于事件探测时存在着诸多不足。
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。
First, the whole system was decomposed into several subsystems by adopting fuzzy k-means cluster.
首先，采用动态聚类方法，将整个系统分解为几个子系统。
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。
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。
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。
At last, the segmentation result is clustered again using K-means cluster to get the ultimate segmentation result.
最后用K均值算法对谱聚类集成的结果进行再次聚类，得到最终的集成聚类分割结果。
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 driver is straightforward to use as a stand-alone program without Hadoop, as demonstrated by running ant k-means.
可以直接将驱动程序作为单独的程序使用，而不需要Hadoop 的支持，比如说您可以直接运行antk-means。
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 recognition method based on HMM and K-means cluster is proposed through extracting LPC characteristic from acoustic target.
提出一种隐马尔可夫模型和K -均值聚类混合模型的声目标识别方法。
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。
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。
I use mathematical statistics analysis, such as confidence intervals, hypothesis testing, K-means Cluster, regression analysis and so on.
运用置信区间分析、假设检验、聚类分析、回归分析等数理统计分析方法。
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。
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。
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。
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。
Experiments using artificial data and actual business data testify the validity of this method. It can improve the traditional K-means effect well.
采用人工数据和实际商业数据的实验证明该方法能有效地改善传统的聚类效果。
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。
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。
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。
In the RBF network, to overcome the defects of traditional K-means scheme with local search, an orthogonal least square algorithm is used to select RBF center.
在RBF网络中，为了克服传统K均值聚类法局部寻优的缺陷，采用了正交最小二乘法选取rBF中心。
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。
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。
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