Adaptive real-time anomaly detection for safeguarding by by Kalle Burbeck.

By by Kalle Burbeck.

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Performing all clustering operations on the grid structure improves processing time. STING [116] is a typical example of a grid-based method. 10: Pure anomaly detection using clustering • Model-based clustering hypothesises a model for each cluster and finds the best fit of the data to that model. One approach is conceptual clustering, which given a set of unlabelled objects produces a classification of those objects. The classifications are often represented by probabilities, one example is the COBWEB [45] method.

With a centralised system, communication bottlenecks can build up around the central controlling node as the network increases in size. Within a decentralised system the communication overhead depends upon the degree of coupling between the distributed components. However, since this overhead is distributed over the whole of the network, even quite tightly coupled distributed systems can generally be scaled more easily. The processing of information is also distributed across many nodes in agent systems.

BIRCH [122] uses hierarchical clustering in its first phase, after which iterative relocation is applied to clusters rather than individual objects in subsequent phases. Two hierarchical approaches exist: – The agglomerative approach starts with each object forming a separate cluster. It successively merges similar clusters together until all clusters are merged into one at the top-most level of the hierarchy. – The divisive approach starts with all objects in the same cluster. In each successive iteration, a cluster is split up into smaller clusters until every object is in a single cluster or until a termination criterion is fulfilled.

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