Anomaly detection

Anomaly Detection may refer to an unsupervised data mining technique that produces a data mining model for identifying cases (records) that deviate from the norm in a dataset. The data provided for model building consists of normal cases from which an anomaly detection algorithm, such as One Class Support Vector Machine, learns normal patterns. Applying the model to data with similar schema and attribute content yields a probability that each case is normal or anomalous.

It may be used, for example, in network intrusion detection and fraud detection.

See specification for JSR-247 [1].