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Anomaly detection identifies unusual patterns in data to detect failures, fraud, or security incidents early. The concept covers statistical techniques, rule-based systems and machine learning, including operations, evaluation and adaptation to concept drift. Deployment requires data preparation, model validation and continuous monitoring. Trade-offs include…
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Anomaly detection identifies observations that deviate markedly from the expected pattern of a dataset or system.
The approach grew from the practical need to find faults, abuse, and unusual behavior early in large data collections. Its statistical and machine-learning development is broad; Scikit-learn and PyOD represent current methods and tools, not a single origin.
First describe a normal range from data or rules. New observations receive a deviation score and are flagged above a threshold. A domain expert then decides whether the deviation is a fault, a rare normal case, or a relevant event.
A model or rule set describes what counts as normal in the given context.
The score expresses how far an observation deviates from the baseline.
A threshold determines which deviations are forwarded as alerts.
People classify flagged cases and distinguish real events from false alarms.
Anomaly detection supports monitoring, fraud detection, quality assurance, and security analysis. Thresholds, data drift, and asymmetric error costs must fit the application context.
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