An anomaly detection algorithm is presented based on improved KFCM cluster algorithm which can achieve partial best partition.

提出了一种改进的KFCM聚类异常检测算法，该算法可获得局部最优划分。

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Finally, the experimental results illustrate the improved KFCM algorithm can achieve good clustering performance and high efficiency for software engineering data mining.

实验结果表明，改进的KFCM算法对软件工程数据的挖掘有很好的聚类效果，且有较高的效率。

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Finally, the experimental results illustrate the improved KFCM algorithm can achieve good clustering performance and high efficiency for software engineering data mining.

实验结果表明，改进的KFCM算法对软件工程数据的挖掘有很好的聚类效果，且有较高的效率。

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