A Review on Outlier Detection Techniques

Abstract

The outlier is unexpected behavior of data. Outlier detection is important in various domains like fraud detection, intrusion detection, activity monitoring, etc. Data is generated continuously on large scale in many applications. There is need to detect outlier from static data as well as streaming data. There are basic two types of outlier: global outlier and local outlier. This work aims to study various local and global outlier detection techniques for static and streaming data. The works also focuses on various local and global outlier detection techniques which are efficient in terms of time and memory.