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 …

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 …

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 …

Outlier detection is an important task in data mining, with applications ranging from intrusion
detection to human gait analysis. With the growing need to analyze high speed data …

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 …

Outlier detection is an important task in many domains and is intensively studied in the past
decade. Further, how to explain outliers, ie, outlier interpretation, is more significant, which …

Why is a given point in a dataset marked as an outlier by an off-the-shelf detection
algorithm? Which feature (s) explain it the best? What is the best way to convince a human …

We address the problem of outlying aspects mining: given a query object and a reference
multidimensional data set, how can we discover what aspects (ie, subsets of features or …