What is the primary goal of anomaly detection?

The primary goal of anomaly detection is to detect rare observations that differ from the norm. Anomaly detection is a critical process in data analysis that focuses on identifying data points, events, or observations that deviate significantly from the expected pattern or distribution within a dataset. These anomalies can indicate important occurrences such as fraud, network intrusions, or other significant events that deviate from normal behavior.

By focusing on these irregularities, anomaly detection helps analysts and organizations respond to unusual activities that might require immediate attention or further investigation. Effective anomaly detection can thus enhance decision-making processes and improve operational efficiencies by highlighting issues that might otherwise go unnoticed.

In context, improving database structure, identifying patterns, or categorizing data into standard forms do not capture the essence of anomaly detection's purpose, which is specifically about finding those outliers or unusual occurrences within data.