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

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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Meanwhile, our segmentation method based on temporal spectral clustering achieves wonderful results.

同时，基于谱聚类的时序分割方法无需训练数据的驱动，也取得了理想的结果。

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The similarity measure of spectral clustering algorithm is one of the research hotspots.

相似性测度是谱聚类算法中的一个研究热点。

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Semi-supervised clustering based on spectral clustering

一种基于谱聚类的半监督聚类方法

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Community-finding Algorithm in Complex Networks Based on Spectral Clustering

基于谱聚类的复杂网络社团发现算法

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Cluster analysis of gene expression profile was performed.

进行了基因表达谱聚类分析和比较；

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