  Anomaly detection is the process of identifying cases, or groups of cases,
that are in some way unusual and do not fit the general patterns present in the
dataset. Numerous algorithms use discretization of numerical data in their
detection processes. This study investigates the effect of the discretization
method on the unsupervised detection of each of the six anomaly types
acknowledged in a recent typology of data anomalies. To this end, experiments
are conducted with various datasets and SECODA, a general-purpose algorithm for
unsupervised non-parametric anomaly detection in datasets with numerical and
categorical attributes. This algorithm employs discretization of continuous
attributes, exponentially increasing weights and discretization cut points, and
a pruning heuristic to detect anomalies with an optimal number of iterations.
The results demonstrate that standard SECODA can detect all six types, but that
different discretization methods favor the discovery of certain anomaly types.
The main findings also hold for other detection techniques using
discretization.
