In data mining, anomaly detection (or outlier detection) is defined as the identification of items, events, or observations that do not conform to an expected pattern (or other items) in a dataset, and that are sometimes referred to as rare events. These events raise suspicion and, typically, the anomalous items will translate to some kind of problem that requires deeper attention and needs to be addressed. Common events include bank fraud, structural defects, medical conditions, or simply mistakes in a text.
Anomalous items that raise suspicions by differing significantly from the majority of the data may also be referred to as outliers, novelties, noise, deviations, and exceptions.
Anomaly detection is a technique or method that is used to identify unusual patterns that don't seem to conform to the accepted behavior. You will routinely see anomaly...
