The last decade has witnessed the explosion of machine learning research studies with the
inception of several algorithms proposed and successfully adopted in different application …

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 …

With a long history of traditional Graph Anomaly Detection (GAD) algorithms and recently
popular Graph Neural Networks (GNNs), it is still not clear (1) how they perform under a …

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 …

Supervised learning techniques are widely employed in credit card fraud detection, as they
make use of the assumption that fraudulent patterns can be learned from an analysis of past …

As an essential task in data mining, outlier detection identifies abnormal patterns in
numerous applications, among which clustering-based outlier detection is one of the most …

We survey unsupervised machine learning algorithms in the context of outlier detection. This
task challenges state-of-the-art methods from a variety of research fields to applications …

Anomaly detection is the process of identifying unexpected items or events in datasets,
which differ from the norm. In contrast to standard classification tasks, anomaly detection is …

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 …

Detecting anomalies in data is a vital task, with numerous high-impact applications in areas
such as security, finance, health care, and law enforcement. While numerous techniques …