And this paper also improved the initial center point's selection of K-Means clustering algorithm.

另对聚类算法初始聚类中心的选取也做了改进。

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Researched the unsupervised anomaly detection methods based on clustering analysis, improved the K-means algorithm.

研究了基于聚类分析的非监督式异常检测方法，并改进了K均值算法用于聚类分析。

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The experimental results show that the proposed algorithm is superior to the improved kernel clustering algorithm and K-means in good astringency, stability and overall optimal solutions.

实验结果表明，使用该算法的聚类比改进的核聚算法、K均值算法等单一方法具有良好的收敛性、稳定性和更高的全局最优。

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