- The detection of unusual or anomalous data is an important
function in automated data analysis or data
mining. However, the diversity of anomaly detection
algorithms shows that it is often difficult to determine
which algorithms might detect anomalies given
any random dataset. In this paper we provide a partial
solution to this problem by elevating the search
for anomalous data in transaction-oriented datasets
to an inspection of the rules that can be produced
by higher order longitudinal/spatio-temporal association
rule mining. In this way we are able to apply
algorithms that may provide a view of anomalies that
is arguably closer to that sought by information analysts.
(from crpit.com)
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