Conferences in Research and Practice in Information Technology
  

Online Version - Last Updated - 20 Jan 2012

 

 
Home
 

 
Procedures and Resources for Authors

 
Information and Resources for Volume Editors
 

 
Orders and Subscriptions
 

 
Published Articles

 
Upcoming Volumes
 

 
Contact Us
 

 
Useful External Links
 

 
CRPIT Site Search
 
    

CURIO: A Fast Outlier and Outlier Cluster Detection Algorithm for Large Datasets

Ceglar, A., Roddick, J.F. and Powers, D.M.W.

    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.
pdf (from crpit.com) pdf (local if available) BibTeX EndNote GS
 

 

ACS Logo© Copyright Australian Computer Society Inc. 2001-2014.
Comments should be sent to the webmaster at crpit@scem.uws.edu.au.
This page last updated 16 Nov 2007