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.

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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Research on Segmentation of Lymphatic Ultrasonic Images Based on the Spectral Cluster Ensemble

不同产地小美牛肝菌的红外光谱聚类分析研究谱聚类集成的淋巴结超声图像分割算法

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