In the filtered image feature, application of K means algorithm to cluster features index, then using the Euclidean distance to limit excessive intensive feature points, which are descriptive of the image feature vector.

K means algorithm applied in vector quantization strongly depends on the selection of the initial codebook, and if not given a good initial codebook it can easily be trapped in local minima.

由于传统的K-均值算法在用于矢量量化时强烈依赖初始码书的选取，如果初始码书选取不好，则很容易陷入局部最小点；

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The method models the region with the joint histogram of local binary pattern and the labels of K means algorithm, and G-statistic is used to measure the difference of two histograms.

算法采用区域的局部二值模式特征和K均值聚类算法获得的影像标记构建联合直方图，对区域进行建模，采用G统计量度量直方图相似性。

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In this paper, an improved K means clustering algorithm is presented to accelerate clustering process with more and more classes becoming stable by judging with neighbor centers nearest to the pixel.

改进后的K-means聚类算法使类内像素只通过和相邻的聚类中心进行距离计算来聚类，由于随着算法的迭代进行，大量类的状态基本固定，因此使得聚类速度不断加快。

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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。

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In this paper, we use direct classification and K means algorithm to distinguish the high cloud, meddle cloud, low cloud and earth ′ s surface.

应用模式识别中区域聚类法即最近邻简单试探法和K-均值聚类算法来完成高云、中云、低云和地表的区分。

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The results illustrate that the best centroids initialization in K means clustering is to select vectors characterized the structure of the dataset.

结果表明：若对基因表达数据进行K-均值聚类分析，最好采用能反映数据结构特征的向量对质心进行初始化。

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Experimental results show that this new classifier can realize high quality unsupervised image classification, which outperforms the traditional K means classifier.

实验结果表明，该种分类器能很好地实现对纹理粗糙程度模式的无监督分类，其分类性能要明显好于传统的K均值分类器。

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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。

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A data matrix based on the signal gain of cellular networks and some criterion functions are designed for K means clustering.

在聚类分解中，以测试点信号增益矩阵构造聚类分解数据，并给出了收敛判定函数和相似度计算方法。

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Fuzzy K means Clustering Algorithm and It's Application Study in Segmentation of MR Head Images

模糊K-均值聚类算法及其在磁共振颅脑图像分割中的应用研究

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It also resolves the initial parameter problem of K means algorithm.

而且解决了K-means聚类的参数选择问题。

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Analysis on K means Clustering Algorithm and Its Application in Teaching Quality of Teacher

K-means聚类算法分析及在教师授课质量评价中的应用

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Genetic k Means Algorithm for Clustering of Large Scale Vector Space

大矢量空间聚类的遗传k-均值算法

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Cluster analysis based on K means and immune algorithm

基于K均值和免疫算法的聚类分析

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Automatic Text Categorization Based on K-Nearest Neighbor

基于K-最近距离的自动文本分类的研究

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K-nearest neighbor classifier and BP neural network classifier are designed for the acoustic target classification.

并设计了KNN和改进的BP神经网络两种分类器。

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The third step directly classifies the ship as one of the five types with k-nearest neighbor method.

第三步使用k-近邻法直接将舰船目标分配到五类中的一类。

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An algorithm for reverse k-nearest neighbor queries based on M-tree in spatial network databases

空间网络数据库中基于M-tree索引的反最近邻查询算法

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K-Nearest Neighbor Nonparametric Regression for probability forecasting with its applications

K近邻非参数回归概率预报技术及其应用

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K-Nearest Neighbor Nonparametric Estimation Bootstrap Model for Weather Probability Forecasting

概率天气预报的K近邻非参数估计仿真模型

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