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 …

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 …

Detecting outliers is a significant problem that has been studied in various research and
application areas. Researchers continue to design robust schemes to provide solutions to …

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 …

Traditional distance and density-based anomaly detection techniques are unable to detect
periodic and seasonality related point anomalies which occur commonly in streaming data …

Many well-established anomaly detection methods use the distance of a sample to those in
its local neighbourhood: so-calledlocal outlier methods', such as LOF and DBSCAN. They …

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 …

This is an overview of the end-to-end data cleaning process. Data quality is one of the most
important problems in data management, since dirty data often leads to inaccurate data …

While machine learning and artificial intelligence have long been applied in networking
research, the bulk of such works has focused on supervised learning. Recently, there has …

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 …