Anomaly detection refers to identification of data items, points or events that are rare, differ significantly from other
data items, points or events or that have unexpected behaviour. These rare items are called anomalies, outliers, exceptions,
defects or contaminants. Anomaly and outliers are the 2 commonly used words. Two assumptions have to hold off for the
anomaly detection to be effective - number of normal patterns should be higher than the no of anomalies and anomalies should
be distinguishable from normal patterns. Anomalies or outliers were detected as a part of cleaning the data. However, it was
later found in 2000 that detection of anomalies can help in solving real world problems.
