Anomaly detection, aka outlier detection or novelty detection, has been a lasting yet active
research area in various research communities for several decades. There are still some …

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

PyOD is an open-source Python toolbox for performing scalable outlier detection on
multivariate data. Uniquely, it provides access to a wide range of outlier detection …

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 …

Clusters of viral pneumonia occurrences over a short period may be a harbinger of an
outbreak or pandemic. Rapid and accurate detection of viral pneumonia using chest X-rays …

Although deep learning has been applied to successfully address many data mining
problems, relatively limited work has been done on deep learning for anomaly detection …

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 …

Video anomaly detection is of critical practical importance to a variety of real applications
because it allows human attention to be focused on events that are likely to be of interest, in …

Large-scale datasets possessing clean label annotations are crucial for training
Convolutional Neural Networks (CNNs). However, labeling large-scale data can be very …