Anomaly Detection

Anomaly detection is a technique used in data analysis to identify unusual patterns that do not conform to expected behavior. It is commonly employed in various fields such as fraud detection, network security, and fault detection in manufacturing systems. The main characteristics of anomaly detection include the ability to process large datasets, use of statistical methods, and machine learning algorithms to distinguish between normal and abnormal observations. It can be unsupervised, where the model learns from data without labeled examples, or supervised, where it requires pre-labeled data for training. Overall, anomaly detection plays a crucial role in ensuring system reliability and security by flagging potential issues before they escalate.