Outlier (or anomaly) detection is an important
problem for many domains, including fraud detection,
risk analysis, network intrusion and medical
diagnosis, and the discovery of significant outliers is
becoming an integral aspect of data mining. This
paper presents CURIO, a novel algorithm that uses
quantisation and implied distance metrics to provide
a fast algorithm that is linear for the number of
objects and only requires two sequential scans of
disk resident datasets. CURIO includes a novel direct
quantisation technique and the explicit discovery
of outlier clusters. Moreover, a major attribute of
CURIO is its simplicity and economy with respect to
algorithm, memory footprint and data structures. |
| Cite as: Ceglar, A., Roddick, J.F. and Powers, D.M.W. (2007). CURIO: A Fast Outlier and Outlier Cluster Detection Algorithm for Large Datasets. In Proc. 2nd International Workshop on Integrating Artificial Intelligence and Data Mining (AIDM 2007), Gold Coast, Queensland, Australia. CRPIT, 84. Ong, K.-L., Li, W. and Gao, J., Eds. ACS. 37-45. |
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