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ESEM
2007
ACM
13 years 11 months ago
An Approach to Outlier Detection of Software Measurement Data using the K-means Clustering Method
The quality of software measurement data affects the accuracy of project manager’s decision making using estimation or prediction models and the understanding of real project st...
Kyung-A Yoon, Oh-Sung Kwon, Doo-Hwan Bae
TJS
2010
182views more  TJS 2010»
13 years 7 months ago
A novel unsupervised classification approach for network anomaly detection by k-Means clustering and ID3 decision tree learning
This paper presents a novel host-based combinatorial method based on k-Means clustering and ID3 decision tree learning algorithms for unsupervised classification of anomalous and ...
Yasser Yasami, Saadat Pour Mozaffari
IPPS
2008
IEEE
14 years 3 months ago
Outlier detection in performance data of parallel applications
— When an adaptive software component is employed to select the best-performing implementation for a communication operation at runtime, the correctness of the decision taken str...
Katharina Benkert, Edgar Gabriel, Michael M. Resch
PKDD
1999
Springer
130views Data Mining» more  PKDD 1999»
14 years 1 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. ...
PAKDD
2009
ACM
149views Data Mining» more  PAKDD 2009»
14 years 1 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