Introduces "axial K-means" algorithm using in documents retrieval.

An improved genetic K-means clustering algorithm based on image segmentation

改进的图像分割遗传K-均值聚类算法

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Tobacco Distribution Area Classification Based on Improved K-means Algorithm

基于改进K-means算法的烟草配送区域划分

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On the K-means Clustering of the Gene Expression Data Based on Genetic Algorithm

基于遗传算法的基因表达数据的K-均值聚类分析

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Experimental results show that K-means method can get good clustering image segmentation effect.

实验结果表明K-means方法可以取得好的聚类分割效果。

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Application on K-means Text Clustering Algorithm in Intelligent Question Answering System

K-均值文本聚类算法在智能答疑系统中应用

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D Point Cloud Model Segmentation Based on K-means Cluster Analysis

基于K-means聚类方法的三维点云模型分割

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Clustering Method of Time Series Based on EMD and K-means Algorithm

基于EMD与K-means算法的时间序列聚类

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Research in Improved Negative Selection Algorithm Based on K-means Clustering

基于K-均值聚类的改进非选择算法研究

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Study and Application of k Value Optimization Based on the k-means Clustering Algorithm

基于k-means算法的k值优化的研究与应用

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Research on Load Model Based on K-Means Clustering Algorithm

基于K-means聚类算法的负荷模型研究

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Research into K-means Clustering Algorithm Based on Dynamic Tunneling System

基于动态隧道系统的K-means聚类算法研究

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A Cluster Analysis Method Based on Improved K-Means Algorithm

一种基于改进的K-Means算法的聚类分析方法

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Research of K-Means Clustering in Data Mining Based on Genetic Algorithm

基于遗传算法的k-means聚类挖掘方法的研究

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A pattern classification of the mean pentad circulations at 500 MB level over East Asia for each NSS by the method of k-mean cluster analysis

应用K均值聚类法对东亚各自然天气季节500毫巴候平均环流的分型试验

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In the process of identification, a search method is presented that the K-mean theory in pattern recognition is used in the cost function, which can promote the efficiency of identification.

辨识过程中，提出一种将模式识别中的K平均理论应用于代价函数搜索的方法，提高辨识效率。

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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.

而后详细介绍主成分的分析技术，分析了K-均值算法和主成分分析算法的相关性，简述前期研究中K-均值算法在无线传感器网络中的应用。

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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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