Outliers are also referred to as abnormalities, discordants, deviants, or anomalies in the data
mining and statistics literature. In most applications, the data is created by one or more …

High‐dimensional data in Euclidean space pose special challenges to data mining
algorithms. These challenges are often indiscriminately subsumed under the term 'curse of …

Detecting outliers is a significant problem that has been studied in various research and
application areas. Researchers continue to design robust schemes to provide solutions to …

In this paper, we introduce autoencoder ensembles for unsupervised outlier detection. One
problem with neural networks is that they are sensitive to noise and often require large data …

An integrated framework for density-based cluster analysis, outlier detection, and data
visualization is introduced in this article. The main module consists of an algorithm to …

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 …

In supervised learning it has been shown that a collection of weak classifiers can result in a
strong classifier with error rates similar to those of more sophisticated methods. In …

In the past two decades, most research on anomaly detection has focused on improving the
accuracy of the detection, while largely ignoring the explainability of the corresponding …

Outlier detection has been a topic in statistics for centuries. Over mainly the last two
decades, there has been also an increasing interest in the database and data mining …

Ensemble analysis has recently been studied in the context of the outlier detection problem.
In this paper, we investigate the theoretical underpinnings of outlier ensemble analysis. In …