VA-Trie: A New and Efficient High Dimensional Index Structure for Approximate k Nearest Neighbor Query

K-neighbor Searching of Surface Reconstruction From Scattered Points

散乱数据点的k近邻搜索算法

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Simulation experiments show that the searching speed of k nearest neighbors is improved.

大量数据的实验结果表明本算法可以大大提高在海量空间数据点中搜索测点k近邻的速度。

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Continuous k-nearest Neighbor Queries Based on Extended Spatio-temporal Distance Metrics

基于扩展时空距离度量的连续k近邻查询方法

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Algorithm for Fast Searching of k-nearest Neighbors in Cloud Points

海量空间数据点k近邻的快速搜索算法

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Scalable processing of multiple continuous k nearest neighbor queries in road networks

可伸缩的道路网络多连续k近邻查询处理

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Research on multi-threading processing of concurrent multiple continuous k-nearest neighbor queries

多用户连续k近邻查询多线程处理技术研究

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Stream Data Outlier Mining Algorithm Based on Reverse k Nearest Neighbors

基于反k近邻的流数据离群点挖掘算法

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Prediction of Protein Subcellular Locations with a Weighted Fuzzy k_NN Algorithm

基于加权模糊k近邻方法的蛋白质亚细胞位点预测

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In dynamic evolution stage we use k-NN evolution and directed sub-networks are obtained. Compared with traditional complex networks, the proposed method contains more information of the shape images, so it is more robust.

在动态演化阶段采用k近邻演化方式，每个演化阶段所得到的子网络都为有向网络，所包含的信息更加充分，能够更加准确的描述形状图像。

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Novel Strategy for Spatial k-NN Query

空间k近邻查询的新策略

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Data stream clustering is an important issue in data stream mining. Most of the existing algorithms adopted K medians ( means) method to solve this problem, which are not suitable to address the problem of clustering high dimensional or abnormal distributed data streams.

数据流聚类是数据流挖掘研究的一个重要内容，已有的数据流聚类算法大多采用k中心点（均值）方法对数据进行聚类，不能对数据分布不规则以及高维空间数据流进行有效聚类。

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Considering the fact that the same data may belong to several clusters to some extent at the same time, the fuzzy membership concept of data is used in the process of clustering, the fuzzy k-harmonic means clustering algorithm is thus proposed.

本文对k调和均值算法进行扩展，考虑到数据点同时对不同聚类的隶属关系，将模糊的概念应用到聚类中，提出了模糊k调和均值&Fuzzy k-Harmonic Means（FKHM）算法。

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This paper proposed the multiple Mean-Variance ratio test of which is based on the "Asset Performance Evaluation with Mean-Variance Ratio" of Bai ( 2008), completing the theory of Mean-Variance Ratio test.

本文在Bai（2008）提出的均值方差比（MVR）检验的基础上，完善了小样本检验的理论，提出了k个均值方差比的多元MVR假设。

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The k-harmonic means clustering algorithm is an effective method to avoid the dependency of the performance of clustering on the initialization of the clustering centers.

k调和均值算法用数据点与所有聚类中心的距离的调和平均替代了数据点与聚类中心的最小距离，是一种减小初始值影响聚类结果的有效的聚类方法。

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On a note on k-th mean value of cubic complements

关于立方幂补数k次均值的注记

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SCLA are soft competition schemes of the learning algorithms, in which the center vectors are trained based on the k means algorithm, and can remedy the problems of the dead node and the sensitivity to initial weight vectors that the latter algorithms have.

SCLA是RBF网络基于k均值方法训练中心向量的学习算法的软竞争格式，它可以克服后者对初始值敏感和死节点的问题。

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Consider water level of boiler drum as the object for study, find out the proper variables as input of the ANN ( Artificial Neural Network) throughout the analysis of the inner mechanism. The function of learning was realized using K-means Cluster method and Gradient Descent Algorithm.

以汽包水位为对象.结合机理分析确定原始变量作为神经网络的输入.通过k均值聚类法则与梯度下降法实现了网络的学习功能.并最终建立了基于RBF神经网络的软测量模型。

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The new algorithm is to firstly divide unlabeled data into many subsets with a new label by k-means clustering, then train the SVMs using the new data set to get decision boundary and support vectors, at last use the SVMs classifier to classify the unlabeled data.

首先利用k均值算法将未知数据划分成某个数量的子集，然后对新数据进行支持向量机训练得到决策边界与支持矢量，最后对无标识数据进行分类。

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In this dissertation, adopting the results acquired by the band selection of hyperspectral image based on ACO, the feature bands are extracted as simulation data, and the clustering method experiment is performed. The experiment results are compared with the results of traditional k-means algorithm.

本文依据基于蚁群算法的高光谱图像波段选择方法获得的选择结果，从中提取若干个特征波段作为数据源，采用上述聚类方法进行实验，并与传统的k均值算法比较实验结果。

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EM clustering algorithm is a popular iterative refinement algorithm which can be regarded as an extension of K-means algorithm. Based on the probability of the occurrence of subordination between objects and clusters, the objects can be allotted by this algorithm.

EM聚类算法是一种流行的迭代求精算法，可以看作是k均值算法的一种扩展，该算法根据对象与簇之间的隶属关系发生的概率来分配对象。

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