12 - Outlier Detection

Abstract

This chapter aims to study outlier detection techniques. The different types of outliers are defined. An overview of outlier detection methods is also presented. Assume that a given statistical process is used to generate a set of data objects. An outlier is a data object that deviates significantly from the rest of the objects, as if it were generated by a different mechanism. Types of outliers include global outliers, contextual outliers, and collective outliers. An object may be more than one type of outlier. Outlier detection (also known as anomaly detection) is the process of finding data objects with behaviors that are very different from expectation. Outlier detection is important in many applications in addition to fraud detection such as medical care, public safety and security, industry damage detection, image processing, sensor/video network surveillance, and intrusion detection. Outlier detection and clustering analysis are two highly related tasks. Clustering finds the majority patterns in a data set and organizes the data accordingly, whereas outlier detection tries to capture those exceptional cases that deviate substantially from the majority patterns. Outlier detection and clustering analysis serve different purposes. Outlier detection methods are organized here by category, are statistical, proximity-based, clustering-based, and classification-based. Mining contextual and collective outliers and outlier detection in high-dimensional data are also discussed.