Recently, the Support Vector Regression (SVR) has been applied in the financial time series prediction. The financial data are usually highly noisy and contain outliers. Detecting ...
Monitoring the performance of large shared computing systems such as the cloud computing infrastructure raises many challenging algorithmic problems. One common problem is to trac...
Chiranjeeb Buragohain, Luca Foschini, Subhash Suri
In this paper, we propose a novel formulation for distance-based outliers that is based on the distance of a point from its kth nearest neighbor. We rank each point on the basis o...
We show that excluding outliers from the training data significantly improves kNN classifier, which in this case performs about 10% better than the best know method--Centroid-based...
This paper deals with finding outliers (exceptions) in large, multidimensional datasets. The identification of outliers can lead to the discovery of truly unexpected knowledge in ...
: 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. ...
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...
Publishing microdata raises concerns of individual privacy. When there exist outlier records in the microdata, the distinguishability of the outliers enables their privacy to be e...
Although the least median of squares (LMedS) method and the least trimmed squares (LTS) method are said to have a high breakdown point (50%), they can break down at unexpectedly l...
Camera motion estimation is useful for a range of applications. Usually, feature tracking is performed through the sequence of images to determine correspondences. Furthermore, ro...