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Publication - Dr Ben Azvine

    Anomaly Detection by Multi-Level Tolerance Relations


    Martin, T & Azvine, B, 2016, ‘Anomaly Detection by Multi-Level Tolerance Relations’.


    A method for partitioning a plurality of entities each associated with a plurality of ordered sequences of events received by a computer system, the method comprising: defining a minimal directed acyclic graph data structure representing the sequences of events to define a plurality of categories of behaviour of the entities; defining a threshold degree of similarity as an xmu number, the xmu number having cardinality that is able to vary across a normalised range; defining a relation for each entity including a degree of association of the entity with each of the categories; defining a cluster of entities as a set of entities comprising a first entity; and comparing a relation for the first entity with a relation for a second entity to define a xmu Jaccard similarity coefficient for the first and second entities; responsive to the coefficient meeting the threshold degree of similarity, adding the second entity to the cluster.

    Full details in the University publications repository