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» Outlier Detection by Rareness Assumption
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GI
2004
Springer
13 years 9 months ago
Outlier Detection by Rareness Assumption
: A concept for identification of candidates for outliers is presented, with a focus on nominal variables. The database concerned is searched for rules that are almost universally...
Tomas Hrycej, Jochen Hipp
NIPS
2004
13 years 5 months ago
Active Learning for Anomaly and Rare-Category Detection
We introduce a novel active-learning scenario in which a user wants to work with a learning algorithm to identify useful anomalies. These are distinguished from the traditional st...
Dan Pelleg, Andrew W. Moore
PKDD
1999
Springer
130views Data Mining» more  PKDD 1999»
13 years 8 months ago
OPTICS-OF: Identifying Local Outliers
: For many KDD applications finding the outliers, i.e. the rare events, is more interesting and useful than finding the common cases, e.g. detecting criminal activities in E-commer...
Markus M. Breunig, Hans-Peter Kriegel, Raymond T. ...
SIGMOD
2000
ACM
137views Database» more  SIGMOD 2000»
13 years 8 months ago
LOF: Identifying Density-Based Local Outliers
For many KDD applications, such as detecting criminal activities in E-commerce, finding the rare instances or the outliers, can be more interesting than finding the common pattern...
Markus M. Breunig, Hans-Peter Kriegel, Raymond T. ...
NIPS
2007
13 years 5 months ago
Nearest-Neighbor-Based Active Learning for Rare Category Detection
Rare category detection is an open challenge for active learning, especially in the de-novo case (no labeled examples), but of significant practical importance for data mining - ...
Jingrui He, Jaime G. Carbonell