Anomaly Detection algorithm falls under the clustering category.
It is used to find data in the system that does not match with existing model of the data. Such anomalies are inconsistent with regards to remaining data and can affect reporting and analysis of data.
The anomalous data is identified by applying K-means clustering algorithm on the data sets and the data farthest from the center of the cluster is identified as an anomaly.
To know more about K-means see PAL Algorithms simplified – K means