… The techniques discussed in this paper extend the applicability of outlier detection techniques
to high dimensional problems; such cases are most valuable from the perspective of data …

… Outlier detection is an integral component of statistical modelling and estimation. For highdimensional
data, … We propose an outlier detection procedure that replaces the classical …

… all outlier models proposed so far inherently unsuitable for the requirements met in mining
high-dimensional data … Aiming to explain why a point is an outlier, we found only one other …

… effective outlier detection method, which is capable of handling high-dimensional data and
… It aims at projecting the high-dimensional neighborhood into a low-dimensional space. The …

… the issue of high-dimensional data in the context of outlier detection. High-dimensional data
can cause serious issues when we apply many of the existing outlier detection methods. In …

… of high-dimensional data and relate these to issues for outlier … dimensionality on outlier
detection in high-dimensional data, … or challenges for outlier detection in high-dimensional data: …

… for detecting outliers in high dimensional data streams. As for Section 5, it introduces the
proposed taxonomies for outlier detection techniques in high dimensional data … of data streams. …

… of the method for high dimensional data sets. To conclude, we presented a distance-based
outlier detection algorithm to deal with high dimensional data sets that scales linearly with …

… data. In this paper, we provide a brief overview of the outlier detection methods for high-dimensional
data… -of-the-art techniques of outlier detection for practitioners. Specifically, we firstly …

… In order to use the concept of local density, we need a meaningful concept of distance for
sparse high-dimensional data; if this does not exist, then the outliers found are unlikely to be …