Outlier detection is an important data analysis task. It is used in many domains to identify interesting and emerging patterns, trends, and anomalies from data. Outlier detection is used to detect anomalies in many different domains, including computer network intrusion; gene expression analysis; disease onset identification, including cancer detection; financial fraud detection; and human behavioral analysis. Among the four primary tasks of data mining, outlier detection is the closest to the motivation of data mining as discovering interesting patterns and modeling relationships are the main aims of data mining research. Outlier detection, also known as anomaly detection, deviation detection, novelty detection, and exception mining, has been widely studied in data mining as well as in statistics and machine learning.