K-means algorithm is a classical clustering algorithm.
平均算法是经典的聚类算法。
This directory contains code implementing the K-means algorithm.
这个目录包含了K - means算法的代码实现。
For increasing classifiers classification rate, We make use of the fuzzy theories to K-means algorithm again.
基于K -均值算法的模糊分类器具有很好的分类效果，用它可以很准确的对训练样本进行分类。
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。
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。
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。
The action potentials' features are extracted by PCA, the action potential classification is implemented by the improved K-means algorithm.
该方法采用PCA提取动作电位特征，使用改进K均值算法实现动作电位分类。
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。
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。
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。
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。
Secondly, the anomaly detection model based on K-means algorithm and SOM network is constructed. It can classify the normal and abnormal network data stream so better to detect the unknown attack.
提出了一种k-均值聚类算法和SOM自组织神经网络算法相结合的异常检测模型，使得系统可以更好的分类正常数据流和异常数据流，以此来防范未知的攻击。
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。
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。
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。
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。
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。
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。
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。
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。
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。
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。
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 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。
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