Data stream is a newly emerging data model for applications like environment monitoring, Web click stream, network traffic monitoring, etc. It consists of an infinite sequence of d...
In this paper we report about an investigation in which we studied the properties of Bayes' inferred neural network classifiers in the context of outlier detection. The proble...
Detecting outliers is an important topic in data mining. Sometimes the outliers are more interesting than the rest of the data. Outlier identification has lots of applications, su...
We review two versions of a topology preserving algorithm one of which we had previously [1] found to be more successful in defining smooth manifolds and tight clusters. In the con...
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...
Outlier detection in wireless sensor networks is essential to ensure data quality, secure monitoring and reliable detection of interesting and critical events. A key challenge for...
: 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. ...
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. ...
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...
We have proposed replicator neural networks (RNNs) as an outlier detecting algorithm [15]. Here we compare RNN for outlier detection with three other methods using both publicly a...
Graham J. Williams, Rohan A. Baxter, Hongxing He, ...