Unsupervised Anomaly Detection With Specified 5

Unsupervised anomaly detection is a technique for identifying abnormal patterns in unlabelled data, particularly suitable for scenarios where the proportion of anomalies is extremely low, such as 1% abnormal. This method learns the distribution characteristics of normal data and automatically detects data points that deviate from the normal pattern without the need for pre-labelled anomaly samples. Its objective is to improve detection accuracy, reduce false positives and false negatives, and it is widely applied in areas like industrial monitoring, cybersecurity, and medical diagnosis, making it an important application technology.