Outlier detection is a statistical procedure that aims to find suspicious events or items that
are different from the normal form of a dataset. It has drawn considerable interest in the field …

Outlier detection is an important task in data mining, with applications ranging from intrusion
detection to human gait analysis. With the growing need to analyze high speed data …

With precipitously growing demand to detect outliers in data streams, many studies have
been conducted aiming to develop extensions of well-known outlier detection algorithm …

In this paper, a novel, parameter-free, incremental local density and cluster-based outlier
factor (iLDCBOF) method is presented that unifies incremental versions of local outlier factor …

Real-time outlier detection in data streams has drawn much attention recently as many
applications need to be able to detect abnormal behaviors as soon as they occur. The arrival …

Outlier detection has recently become an important problem in many industrial and financial
applications. This problem is further complicated by the fact that in many cases, outliers have …

Continuous outlier detection in data streams has important applications in fraud detection,
network security, and public health. The arrival and departure of data objects in a streaming …

Over the past decade, we have witnessed an enormous amount of research effort dedicated
to the design of efficient outlier detection techniques while taking into consideration …

Outlier detection is an important research problem in data mining that aims to discover
useful abnormal and irregular patterns hidden in large datasets. In this paper, we present a …

Anomaly detection is considered an important data mining task, aiming at the discovery of
elements (known as outliers) that show significant diversion from the expected case. More …