Title :
Grouping Categorical Anomalies
Author :
Gebski, Matthew ; Penev, Alex ; Wong, Raymond K.
Author_Institution :
NICTA, Univ. of New South Wales, Sydney, NSW
Abstract :
We present an approach for discovery of groups of unusual data points that are anomalous for similar reasons. This differs from clustering in that the points that are grouped may be quite ´distant´ and can use categorical attributes, and differs from anomaly detection in that we are not looking for individual outliers.
Keywords :
security of data; anomaly detection; categorical anomalies grouping; categorical attributes; Eyes; Hair; Inspection; Intelligent agent; Joining processes; Probability; anomaly; categorical data; data mining; outlier;
Conference_Titel :
Web Intelligence and Intelligent Agent Technology, 2008. WI-IAT '08. IEEE/WIC/ACM International Conference on
Conference_Location :
Sydney, NSW
Print_ISBN :
978-0-7695-3496-1
DOI :
10.1109/WIIAT.2008.162
