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 …

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 …

Anomaly detection is a problem with applications for a wide variety of domains; it involves
the identification of novel or unexpected observations or sequences within the data being …

Anomaly detection is an important problem that has been researched within diverse
research areas and application domains. Many anomaly detection techniques have been …

Data Mining: Concepts and Techniques, Fourth Edition introduces concepts, principles, and
methods for mining patterns, knowledge, and models from various kinds of data for diverse …

Outlier detection has been used for centuries to detect and, where appropriate, remove
anomalous observations from data. Outliers arise due to mechanical faults, changes in …

Abstract Machine learning (ML) is a burgeoning field of medicine with huge resources being
applied to fuse computer science and statistics to medical problems. Proponents of ML extol …

One of the opening steps towards obtaining a reasoned analysis is the detection of outlaying
observations. Even if outliers are often considered as a miscalculation or noise, they may …

Novelty detection is the identification of new or unknown data or signal that a machine
learning system is not aware of during training. Novelty detection is one of the fundamental …

Anomaly or outlier detection is consider as one of the vital application of data mining, which
deals with anomalies or outliers. Anomalies are considered as data points that are …