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 …

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 …

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 …

Current techniques for explaining outliers cannot tell what caused the outliers. We present a
formal method to identify" root causes" of outliers, amongst variables. The method requires a …

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 …

Machine learning (ML) and Deep Learning (DL) methods are being adopted rapidly,
especially in computer network security, such as fraud detection, network anomaly detection …

As we increasingly delegate important decisions to intelligent systems, it is essential that
users understand how algorithmic decisions are made. Prior work has often taken a …

Outlier algorithms are becoming increasingly complex. Thereby, they become much less
interpretable to the data scientists applying the algorithms in real-life settings and to end …

Detecting anomalies in the data is a common machine learning task, with numerous
applications in the sciences and industry. In practice, it is not always sufficient to reach high …