Fig 1.
The result of X-means clustering on standard Euclidean distance from the abnormal cluster centers to the normal cluster center.
(a): The result of K-Means clustering. (b): The final anomaly detection result of the generated data bu using X-iForest. (c): Demonstration of X-iForest on a test dataset.
Table 1.
Statistical characteristics of the other experimental dateset.
Fig 2.
The performance of iForest under different contamination parameters in the dataset with a abnormal ratio of 0.32, c represents the contamination parameter.
Fig 3.
The AUC results of the proposed algorithm X-iForest and others algorithms.
Table 2.
AUC of X-iForest and other algorithms.
Fig 4.
The ADR results of the proposed algorithm X-iForest and others algorithms.
Table 3.
ADR of X-iForest and other algorithms.