Anomaly detection method by clustering normal data

A new anomaly detection method was proposed based on positive selection.The method learned the characteristic of "self" space by clustering,and then selected typical samples from every cluster to construct detectors.And positive selection was used to detect anomalies.The new algorithm is not only effective in certain application with large number of "self" samples,but also avoids the shortcoming by randomly selecting sample in VDetector.Experimental results on Ring data and biomedical data show that the new method is more effective in anomaly detection.