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.

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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Also, because removing the noise involved in this article is differ from traditional de-noising, coupled with the requirements in real-time system itself of study for the practical problems, we proposed a removing noise method based on the k means clustering.

由于本文中涉及的去除噪声的实际问题和传统的意义上的去噪有所区别，结合系统本身实时性的要求，提出了基于k均值聚类的去除底噪声方法。

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The Bag of Words framework based on SIFT was analysised. The support vector machine was used for feature selection point of the visual vocabulary. The experiments show better performance than the k-means clustering algorithm.

以此为基础，研究了SIFT的词袋算法框架，通过支持向量机选择视觉词汇的特征点，实验表明，性能优于k均值聚类算法。

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In order to overcome its shortcomings, this paper proposed an improved incremental K-means ( IIKM) for detecting events.

为了克服其缺陷，本文提出了一种用于事件探测的改进的增量k均值算法（IIKM）。

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In this method, k-means algorithm is employed for finding the clusters of each class, thus the final recognition results are unstable, not optimal and depend on the initial cluster centers.

然而，由于该方法采用的子类划分方法是k均值聚类算法，因而不能保证最终的识别结果是稳定的、最优的、且依赖聚类初始中心的选择。

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In this paper, a credit rating model based on soft computing is proposed which is a integration of fuzzy mathematics and genetic algorithm and use Fast Genetic k means Algorithm to cluster.

提出了一种基于软计算的企业资信评估模型，它集成模糊数学和遗传算法，用快速遗传k均值算法进行聚类。

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Thirdly, the clustering algorithm using the kd-tree data structure was applied to the texture segmentation, and then a fast texture feature clustering effect was achieved.

纹理分割则利用kd树作为数据结构来运行k均值聚类算法从而实现纹理图像的快速分割。

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Because of false classification of feature samples by k-means clustering analysis, a kernel based dynamic clustering method was presented to distinguish different working modes of an air compressor.

针对k均值聚类对特征样本划分存在误分类的问题，提出用基于核的动态聚类算法对风机不同工作状态进行分类识别。

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The experimental results show that the algorithm not only avoids the local optima and is robust to initialization, but also increases the convergence speed and has global searching capability.

实验结果证明，该算法有很好的全局收敛性，不仅有效地克服了传统的k均值算法易陷入局部极小值和对初始值敏感的问题，而且具有较快的收敛速度。

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After analyzing the disadvantages of the classical k-means clustering algorithm, this paper proposes a novel hybrid clustering algorithm incorporating Particle Swarm Optimization into k-means algorithm.

本文在分析k均值聚类算法存在问题的基础上，用粒子群算法优化k均值聚类算法，提出了一种新的混合聚类算法。

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K-means algorithm possesses strong local search capability and rapid convergence capability, and the clustering result is independent of the order of the sample data.

k均值算法的局部搜索能力强、收敛速度快，且聚类结果不受样本数据输入顺序的影响。

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Because we cannot predict the number of targets and the interference caused by noise, we cannot use fixed number clustering algorithm.

由于事先不知道有多少个目标，也无法预测干扰所带来的噪声，因此就不能用类数目固定的k均值聚类。

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The two fuzzy clustering algorithms are mainly analyzed: fuzzy clustering algorithm based on equivalence relation and fuzzy k-means clustering algorithm.

重点分析了两种常用的模糊聚类算法：基于等价关系的模糊聚类和模糊k均值聚类。

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Application of k-means clustering analysis in process improvement

k均值聚类分析在过程改进中的应用

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An Effective Distributed k-Means Clustering Algorithm Based on the Pretreatment of Vectors' Inner-Product

基于向量内积不等式的分布式k均值聚类算法

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Hybrid Particle Swarm Optimization employing improved k-means clustering analysis strategy

采用改进的k均值聚类分析策略的粒子群算法

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Application of an improved k-means algorithm based on outliers and original clustering center

基于孤立点和初始质心选择的k均值算法的改进与应用

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There are lots of drawbacks to traditional incremental K-means in event detection.

传统的增量k均值法用于事件探测时存在着诸多不足。

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We defined the module as a collection of genes with similar expression behaviors.

我们把共享相似表达行为的基因作为一个转录模块，可以把已知的表达谱聚类作为模块进行研究。

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