While advances in computing resources have made processing enormous amounts of data
possible, human ability to identify patterns in such data has not scaled accordingly. Efficient …

Outliers are also referred to as abnormalities, discordants, deviants, or anomalies in the data
mining and statistics literature. In most applications, the data is created by one or more …

Outlier detection refers to the identification of rare items that are deviant from the general
data distribution. Existing approaches suffer from high computational complexity, low …

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 …

Outlier detection refers to the identification of data points that deviate from a general data
distribution. Existing unsupervised approaches often suffer from high computational cost …

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 …

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 …

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 unsupervised outlier ensembles, the absence of ground truth makes the combination of
base outlier detectors a challenging task. Specifically, existing parallel outlier ensembles …

We consider the problem of anomaly detection with a small set of partially labeled anomaly
examples and a large-scale unlabeled dataset. This is a common scenario in many …