Home > Data Mining Algorithms > Anomaly Detection
Anomaly Detection (AD) identifies cases that are unusual within data that is seemingly homogeneous. Anomaly detection is an important tool for detecting fraud, network intrusion, and other rare events that may have great significance but are hard to find.
Oracle Data Mining uses Support Vector Machine (SVM) as the one-class classifier for Anomaly Detection (AD). When SVM is used for anomaly detection, it has the classification mining function but no target. For more information about SVM, see Support Vector Machine Algorithms.
There are two ways search for anomalies:
Build and apply an Anomaly Detection model, as described in these topics.
Use an Anomaly Detection Query, one of the Predictive Query nodes.
To build an AD model, use an Anomaly Detection Node connected to an appropriate data source.
See Applying Anomaly Detection Models for information about how to use AD models to make predictions.