A statistical technique that determines what patterns are normal and then identifies items that do not conform to those patterns. It is applicable in intrusion detection, fraud detection, system health monitoring, event detection in sensor networks, and detecting eco-system disturbances.
Unlike simple classification where the classes are known in advance, in anomaly detection the users don’t know what they are looking for in the data. Anomaly detection is applicable in a variety of domains, such as intrusion detection, fraud detection, fault detection, system health monitoring, event detection in sensor networks, and detecting Eco-system disturbances. It is often used in preprocessing to remove anomalous data from the dataset. In supervised learning, removing the anomalous data from the dataset often results in a statistically significant increase in accuracy.