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TKDE
2002
136views more  TKDE 2002»
13 years 4 months ago
Analyzing Outliers Cautiously
Xiaohui Liu, Gongxian Cheng, John Xingwang Wu
ECAI
2006
Springer
13 years 8 months ago
Agents with Anticipatory Behaviors: To Be Cautious in a Risky Environment
This work presents some anticipatory mechanisms in an agent architecture, modeling affective behaviours as effects of surprise. Through experiment discussion, the advantages of bec...
Cristiano Castelfranchi, Rino Falcone, Michele Piu...
TSMC
2011
228views more  TSMC 2011»
12 years 12 months ago
Privacy-Preserving Outlier Detection Through Random Nonlinear Data Distortion
— Consider a scenario in which the data owner has some private/sensitive data and wants a data miner to access it for studying important patterns without revealing the sensitive ...
Kanishka Bhaduri, Mark D. Stefanski, Ashok N. Sriv...
PKDD
1999
Springer
130views Data Mining» more  PKDD 1999»
13 years 9 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. ...
BMCBI
2006
187views more  BMCBI 2006»
13 years 5 months ago
Detecting outliers when fitting data with nonlinear regression - a new method based on robust nonlinear regression and the false
Background: Nonlinear regression, like linear regression, assumes that the scatter of data around the ideal curve follows a Gaussian or normal distribution. This assumption leads ...
Harvey J. Motulsky, Ronald E. Brown