Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

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 …

Concept drift poses a critical challenge to deploy machine learning models to solve practical
security problems. Due to the dynamic behavior changes of attackers (and/or the benign …

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 …

Learning expressive low-dimensional representations of ultrahigh-dimensional data, eg,
data with thousands/millions of features, has been a major way to enable learning methods …

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 …

Ensembles for unsupervised outlier detection is an emerging topic that has been neglected
for a surprisingly long time (although there are reasons why this is more difficult than …

While many techniques for outlier detection have been proposed in the literature, the
interpretation of detected outliers is often left to users. As a result, it is difficult for users to …

We analyse the interplay of density estimation and outlier detection in density-based outlier
detection. By clear and principled decoupling of both steps, we formulate a generalization of …