Anomaly detection is the process of identifying patterns or observations in data that do not conform to expected behavior. It is used to identify unusual events, incidents, or observations that deviate from the norm.
Anomaly detection algorithms typically learn the underlying patterns and structure of the data and use this knowledge to identify instances that deviate from what is considered normal. The goal is to flag the potentially abnormal instances for further investigation.
There are several methods for performing anomaly detection, including:
Statistical methods: These methods analyze the statistical properties of the data and identify instances that deviate from the normal distribution.
Distance-based methods: These methods calculate the distance between instances and the nearest neighbors in the dataset and identify instances that are far away from their nearest neighbors.
Clustering-based methods: These methods group similar instances and identify instances that do not belong to any groups.
Neural network-based methods: These methods use neural networks to model the underlying patterns of the data and identify instances that do not conform to the learned patterns.
Anomaly detection is a valuable tool in various application areas, such as Fraud detection and credit card fraud. Network intrusion detection, by identifying unusual network activity. System health monitoring, to identify abnormal behavior in servers and other equipment. Manufacturing, detecting defects in products.
In summary, anomaly detection is the process of identifying patterns or observations in data that do not conform to expected behavior. It is used to identify unusual events, incidents, or observations that deviate from the norm, there are several methods for performing anomaly detection, like Statistical methods, Distance-based methods, Clustering-based methods, and Neural network-based methods, and it has a lot of applications in various domains.
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