Anomalies

Anomalies refer to the observations or data points that deviate significantly from the expected or normal behavior of a system. In data science and artificial intelligence, anomalies are often detected through anomaly detection techniques, which involve identifying patterns or outliers in the data that do not conform to the expected behavior. Anomalies can be caused by various factors such as errors in data collection, measurement errors, or even intentional fraud. Anomaly detection is an important task in many applications such as fraud detection, intrusion detection, and predictive maintenance. It involves the use of statistical and machine learning techniques such as clustering, classification, and regression to identify and classify anomalies in the data.
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