Then, This paper introduce Principal Component Analysis theory, give the relation between K-mean and Principal Component Analysis algorithm, describe preliminary studies on K-means algorithm in WSN application.

According to the principle, the K-Mean cluster algorithm is adopted. The performance is validated by the actual data, and the selection of class number is studied.

依据该原则，采用K-Mean聚类算法实现了局部异常检测，利用实际数据验证了算法性能，并对聚类中类别数的选择问题进行了研究。

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K-Mean Gathers a Kind of Analysis on the Attack Abilities in the 18th Session of World Cup Soccer Game Each Participating Team

K-Mean聚类分析对第18届世界杯足球赛各参赛队进攻能力的研究

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Since K-mean algorithm converges quickly while easily getting into local optimum and difference evolution can avoid the disadvantage of the K-mean algorithm.

由于K-mean算法具有收敛快的优点但易陷入局部最优，而差分进化算法可避免K-mean算法陷入局部最优。

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Experiment results indicate that the proposed algorithm can save at least 1/ 3 multiplications relative to the basic K-mean clustering algorithm.

实验表明，相对于基本的K均值聚类算法，该方法可以节约1/3以上的计算量。

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PP k-Mean Clustering under Least Deviation

最小一乘下的PP k－聚类

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Through the research and analysis of wireless sensor network ( WSN) routing, introduce k-mean clustering algorithm for clustering nodes.

通过对无线传感器网络（WSN）路由的研究与分析，引入k-平均聚类算法对节点进行分簇。

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Utilizing the principal component and K-mean cluster analysis, the summer rain belts in eastern China have been classified.

利用主分量分析和K-均值聚类分析的方法，对我国东部夏季雨带进行客观的气候分类。

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K-mean supervised cluster was done for 10 training cases and additional 20 testing cases based on the set of differential genes.

基于差异表达基因分别对10例分析病例和另外20例验证病例进行k-mean聚类分析；

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We used the improved K-mean method to estimate the parameters and the margin linear method for smoothing parameter results in frequency statistics.

在统计词频方面，本文使用了改进的K均值方法对参数进行估计，并采用线性差值法对参数结果进行平滑处理。

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The main study of article is: ( 1) A fast method for fractal image coding based on K-mean clustering is proposed.

主要工作如下：（1）基于K均值聚类的快速编码算法。

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The K-mean cluster analysis is used to classify the process data and obtain the data sets under various steady operating condition.

提出了一种适用于火电厂生产过程的改进PCA故障检测方法：首先用K均值聚类分析方法对过程数据进行分类得到各稳态工况下的数据；

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K-mean methods are usually sensitive to initial clustering centers and propose bad results for irregular distributed samples.

K-均值算法受初始聚类中心的选择影响较大，对于不规则分布的样本往往聚类的效果不佳。

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The k-mean cluster is used for the clustering of the time series. The results show the efficiency of the algorithm.

将k-平均（k-mean）聚类算法应用于分段线性化表示的时间序列，聚类结果表明算法非常有效。

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Fast Fractal Coding by Combining K-Mean Clustering and KD-Tree Search

结合K均值聚类和KD-Tree搜索的快速分形编码方法

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A K-mean and Markov random field combined classification algorithm is presented for the coherence map classification.

提出了基于K-均值-Markov随机场的干涉SAR相关系数图组合分类算法。

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The K-mean clustering algorithm is improved in class centers initialization for fast convergence.

同时对K-均值聚类算法的初始值设定进行了改进以提高收敛速度。

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Segmentation of Caption Region Using Wavelet Transform and K-Mean Clustering

利用小波变换和K均值聚类实现字幕区域分割

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Application of Fuzzy of K-mean Cluster Analysis on the Classification Research of Acid-base Properties of Compounds

模糊K均值聚类分析在化合物酸碱性分类研究中的应用

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Metal Artifact Reduction in CT Using K-Mean Cluster Algorithm

基于K均值聚类算法的CT图像金属伪影去除

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K-mean distance outlier factor detect for outlier pattern of time series

时间序列异常模式的k-均距异常因子检测

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