A Survey of Outlier Detection Methodologies

A Survey of Outlier Detection Methodologies and Their Applications

Outlier detection is a data analysis method and has been used to detect and remove anomalous observations from data. In this paper, we firstly introduced some current mainstream outlier detection methodologies, i.e. statistical-based, distance-based, and density-based. Especially, we analyzed distance-based approach and reviewed several kinds of peculiarity factors in detail. Then, we introduce...

Outlier Detection : A Survey

Outlier detection has been a very important concept in the realm of data analysis. Recently, several application domains have realized the direct mapping between outliers in data and real world anomalies, that are of great interest to an analyst. Outlier detection has been researched within various application domains and knowledge disciplines. This survey provides a comprehensive overview of e...

Outlier Detection Methodologies in Heterogeneous Information Networks

Advancements of Outlier Detection: A Survey

Outlier detection is an important research problem in data mining that aims to find objects that are considerably dissimilar, exceptional and inconsistent with respect to the majority data in an input database [50]. Outlier detection, also known as anomaly detection in some literatures, has become the enabling underlying technology for a wide range of practical applications in industry, busines...

Outlier Detection in WSN- A Survey

In the field of wireless sensor networks, the measurements that deviate from the normal behaviour of sensed data are taken to be as outliers. The potential sources of outliers can be noise and errors, events, and malicious attacks on the network. This paper give an overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Also, a technique-based ...