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, also named as anomaly detection, is one of the hot issues in the field of
data mining. As well-known outlier detection algorithms, Isolation Forest (iForest) and Local …

Data are naturally collected in their raw state and must undergo a series of preprocessing
steps to obtain data in their input state for Artificial Intelligence (AI) and other applications …

Anomaly detection on multivariate time series (MTS) is of great importance in both data
mining research and industrial applications. While a handful of anomaly detection models …

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 …

The rapid development in construction industry, induce a large amounts of concrete data
that are usually measured and analyzed everyday naming that concrete is the second …

Traffic anomaly detection is an essential part of an intelligent transportation system.
Automatic traffic anomaly detection can provide sufficient decision‐support information for …

Outlier detection is critical in real world. Due to the existence of many outlier detection
techniques which often return different results for the same data set, the users have to …

Anomaly detection in multivariate data is an import research field. Many studies have been
proposed aiming to develop the local outlier factor (LOF). However, the existing LOF-based …

Unsupervised techniques typically rely on the probability density distribution of the data to
detect anomalies, where objects with low probability density are considered to be abnormal …