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PAKDD
2009
ACM
149views Data Mining» more  PAKDD 2009»
14 years 2 months ago
A New Local Distance-Based Outlier Detection Approach for Scattered Real-World Data
Detecting outliers which are grossly different from or inconsistent with the remaining dataset is a major challenge in real-world KDD applications. Existing outlier detection met...
Ke Zhang, Marcus Hutter, Huidong Jin
AUSAI
2009
Springer
14 years 5 months ago
A Graph Distance Based Structural Clustering Approach for Networks
In the era of information explosion, structured data emerge on a large scale. As a description of structured data, network has drawn attention of researchers in many subjects. Netw...
Xin Su, Chunping Li
BMCBI
2006
187views more  BMCBI 2006»
13 years 10 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
KDD
2008
ACM
234views Data Mining» more  KDD 2008»
14 years 10 months ago
Angle-based outlier detection in high-dimensional data
Detecting outliers in a large set of data objects is a major data mining task aiming at finding different mechanisms responsible for different groups of objects in a data set. All...
Hans-Peter Kriegel, Matthias Schubert, Arthur Zime...
PKDD
1999
Springer
130views Data Mining» more  PKDD 1999»
14 years 2 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. ...