Outlier detection refers to the identification of data points that deviate from a general data
distribution. Existing unsupervised approaches often suffer from high computational cost …

Most existing approaches to outlier detection are based on density estimation methods.
There are two notable issues with these methods: one is the lack of explanation for outlier …

The evaluation of unsupervised outlier detection algorithms is a constant challenge in data
mining research. Little is known regarding the strengths and weaknesses of different …

This paper demonstrates that the performance of various outlier detection methods is
sensitive to both the characteristics of the dataset, and the data normalization scheme …

Benchmarking unsupervised outlier detection is difficult. Outliers are rare, and existing
benchmark data contains outliers with various and unknown characteristics. Fully synthetic …

Outlier detection is crucial for improving the performance of machine learning algorithms
and is particularly vital in data sets possessing a small number of points. While the existing …

Ensemble methods for classification have been effectively used for decades, while for outlier
detection it has only been studied recently. In this work, we design a new ensemble …

Detecting anomalous objects from given data has a broad range of real-world applications.
Although there is a rich number of outlier detection algorithms, most of them involve hidden …

Detecting outliers in data is an important problem with interesting applications in a myriad of
domains ranging from data cleaning to financial fraud detection and from network intrusion …

Outlier detection methods automatically identify instances that deviate from the majority of
the data. In this paper, we propose a novel approach for unsupervised outlier detection …