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ESEM
2007
ACM
13 years 6 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 3 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
13 years 11 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»
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. ...
PAKDD
2009
ACM
149views Data Mining» more  PAKDD 2009»
13 years 9 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