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 …

Any paper proposing a new algorithm should come with an evaluation of efficiency and
scalability (particularly when we are designing methods for “big data”). However, there are …

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 …

Anomaly detection in hyperspectral image (HSI) is affected by redundant bands and the
limited utilization capacity of spectral–spatial information. In this article, we propose a novel …

Given the prior information of the target, hyperspectral target detection focuses on exploiting
spectral differences to separate objects of interest from the background, which can be …

The development and applications of mobile communication technologies in intelligent
autonomous transportation systems have led to an extraordinary rise in the mount of …

Abstract Data-driven Anomaly Detection approaches have received increasing attention in
many application areas in the past few years as a tool to monitor complex systems in …

This paper introduces the first generic version of data dependent dissimilarity and shows
that it provides a better closest match than distance measures for three existing algorithms in …

Anomaly detection is an important field in data science that has been widely researched and
applied, generating many methods. Among these methods, the isolation forest algorithm is …

Abstract iForest uses a collection of isolation trees to detect anomalies. While it is effective in
detecting global anomalies, it fails to detect local anomalies in data sets having multiple …