What is anomaly detection in high-dimensional data mining systems?

It identifies rare patterns in high-dimensional feature spaces where distance metrics fail.

In high-dimensional spaces, traditional distance-based anomaly detection becomes ineffective due to distance concentration. Modern systems use subspace methods, autoencoders, and probabilistic models to identify anomalies. These approaches learn compressed representations where anomalies stand out more clearly.