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 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 this paper, we introduce autoencoder ensembles for unsupervised outlier detection. One
problem with neural networks is that they are sensitive to noise and often require large data …

This textbook explores the different aspects of data mining from the fundamentals to the
complex data types and their applications, capturing the wide diversity of problem domains …

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 …

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 …

Anomalies are occurrences in a dataset that are in some way unusual and do not fit the
general patterns. The concept of the anomaly is typically ill defined and perceived as vague …

Ensembles for unsupervised outlier detection is an emerging topic that has been neglected
for a surprisingly long time (although there are reasons why this is more difficult than …

Ensemble analysis is a widely used meta-algorithm for many data mining problems such as
classification and clustering. Numerous ensemble-based algorithms have been proposed in …