Detecting Anomaly Collections using Extreme Feature Ranks

• Main Authors: DAI, Hanbo, ZHU, Feida, LIM, Ee Peng, PANG, Hwee Hwa
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2014
• Subjects: Anomaly collection; Extreme feature rank; Anomaly cluster; Outlier group; Spam detection; Spam cluster; Computer Sciences; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/2534; https://ink.library.smu.edu.sg/context/sis_research/article/3534/viewcontent/Detecting_Anomaly_Collections_using_Extreme_Feature_Ranks__edited_.pdf
• Institution: Singapore Management University

Detecting anomaly collections is an important task with many applications, including spam and fraud detection. In an anomaly collection, entities often operate in collusion and hold different agendas to normal entities. As a result, they usually manifest collective extreme traits, i.e., members of a...