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。
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。
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。
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。
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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