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。
The experiment results show that, KFCM-II algorithm is not better than FCM algorithm when applied to segment MRI images with low level degraded conditions;
实验结果表明，KFCM-II算法对低退化条件的MRI图像的分割任务，结果并不能比FCM算法占优；
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。
A comparison of FCM and KFCM-II algorithm with application in MRI image segmentation has been presented, and a discussion on the effect of intensity bias field correction in KFCM-II algorithm given.
对FCM和KFCM - II算法应用于MRI图像分割进行了比较，讨论了在这两种算法中应用灰度有偏场纠正的效果。
A comparison of FCM and KFCM-II algorithm with application in MRI image segmentation has been presented, and a discussion on the effect of intensity bias field correction in KFCM-II algorithm given.
对FCM和KFCM - II算法应用于MRI图像分割进行了比较，讨论了在这两种算法中应用灰度有偏场纠正的效果。
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