document: Methods for anomaly detection in a local context are the conceptual opposite to the afore-described centralized methods, which rely on globally shared models. In data mining, the notion of locality is often given as distance between data values (given a specific distance metric such as Euclidean distance). A data point is compared to the value of its nearest neighbors in terms of data distance [42]. However, the notion of locality can also be given in a geographical distance between the sources of the data. Many similar values (i.e., data with small distance among each other) result in a higher density, called clusters, while values that are less similar result in a lower density. Anomalies can fall outside of any cluster but, when frequently occurring, can form a cluster too. Determining if a datum is normal or anomalous compared to local neighborhood data is a challenge.
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