K-means algorithm is a classical clustering algorithm.

平均算法是经典的聚类算法。

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Traditional K-Means algorithm is sensitive to the initial centers and easy to get stuck at locally optimal value.

传统K均值算法对初始聚类中心敏感，聚类结果随不同的初始输入而波动，容易陷入局部最优值。

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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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