What type of methods seek unique or previously unobserved data patterns?

Novelty detection is a method designed specifically to identify unique or previously unobserved data patterns. This approach is particularly useful in scenarios where one is interested in detecting outliers or anomalies in a dataset. Unlike traditional classification methods that rely on predefined categories, novelty detection focuses on discovering new insights that have not been seen before, enabling analysts to find significant deviations from established patterns.

In novelty detection, the algorithm learns from the normal data and then assesses new data points to determine if they conform to that learned pattern or if they are anomalies. This is crucial in applications such as fraud detection, where recognizing new fraudulent behavior is essential to effective monitoring.

While clustering groups similar data points together, data reduction simplifies datasets without focusing primarily on new patterns, and sequential analysis examines data points in a time-related sequence, none of these methods specifically target the identification of previously unobserved patterns in the same way that novelty detection does.