Anomaly Detection: Methods and Applications | AI Knowledge Hub

Locating Unusual Patterns in Data

Anomaly detection (Anomaly Detection) is the process of identifying rare objects, events, or observations that significantly differ from most data. Anomalies, also known as outliers, spikes, or novelties, can indicate critical events: fraud, equipment failures, cyberattacks, medical issues. This is one of the most important tasks in machine learning with wide applications in finance, security, industry and many other sectors.

An anomaly is an observation that significantly differs from other observations in a dataset. Anomalies can arise due to various reasons: data errors, system changes, rare events, or deliberate actions.

High Reconstruction Error = Anomaly

Effective for complex data

Advantages: Can learn complex patterns

Epidemiology: Detecting Disease Outbreaks

Medical errors: Unusual treatment patterns

5. Retail and e-commerce

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